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101 Best Computer Science Topics for 2023

Any student will know the difficulty that comes with developing and choosing a great topic in computer science. Generally speaking, a good topic should be original, interesting, and challenging. It should push the limits of the field of study while still adequately answering the main questions brought on by the study.

We understand the stress that this may cause students, which is why we’ve dedicated our time to search the web and print resources to find the latest computer science topics that create the biggest waves in the field. Here’s the list of the top computer science research topics for 2023 you can use for an essay or senior thesis :

AP Computer Science Topics for Students Entering College

How has big data impacted the way small businesses conduct market research?
Does machine learning negatively impact the way neurons in the brain work?
Did biotech change how medicine is administered to patients?
How is human perception affected by virtual reality technologies?
How can education benefit from using virtual reality in learning?
Are quantum computers the way of the future or are they just a fad?
Has the Covid-19 pandemic delayed advancements in computer science?

Computer Science Research Paper Topics for High School

How successful has distance learning computer tech been in the time of Covid-19?
Will computer assistance in businesses get rid of customer service needs?
How has encryption and decryption technology changed in the last 20 years?
Can AI impact computer management and make it automated?
Why do programmers avoid making a universal programming language?
How important are human interactions with computer development?
How will computers change in the next five to ten years?

Controversial Topics in Computer Science for Grad Students

What is the difference between math modeling and art?
How are big-budget Hollywood films being affected by CGI technologies?
Should students be allowed to use technology in classrooms other than comp science?
How important is it to limit the amount of time we spend using social media?
Are quantum computers for personal or home use realistic?
How are embedded systems changing the business world?
In what ways can human-computer interactions be improved?

Computer Science Capstone Project Ideas for College Courses

What are the physical limitations of communication and computation?
Is SCRUM methodology still viable for software development?
Are ATMs still secure machines to access money or are they a threat?
What are the best reasons for using open source software?
The future of distributed systems and its use in networks?
Has the increased use of social media positively or negatively affected our relationships?
How is machine learning impacted by artificial intelligence?

Interesting Computer Science Topics for College Students

How has Blockchain impacted large businesses?
Should people utilize internal chips to track their pets?
How much attention should we pay to the content we read on the web?
How can computers help with human genes sequencing?
What can be done to enhance IT security in financial institutions?
What does the digitization of medical fields mean for patients’ privacy?
How efficient are data back-up methods in business?

Hot Topics in Computer Science for High School Students

Is distance learning the new norm for earning postgraduate degrees?
In reaction to the Covid-19 pandemic should more students take online classes?
How can game theory aid in the analysis of algorithms?
How can technology impact future government elections?
Why are there fewer females in the computer science field?
Should the world’s biggest operating systems share information?
Is it safe to make financial transactions online?

Ph.D. Research Topics in Computer Science for Grad Students

How can computer technology help professional athletes improve performance?
How have Next Gen Stats changed the way coaches game plan?
How has computer technology impacted medical technology?
What impact has MatLab software had in the medical engineering field?
How does self-adaptable application impact online learning?
What does the future hold for information technology?
Should we be worried about addiction to computer technology?

Computer Science Research Topics for Undergraduates

How has online sports gambling changed IT needs in households?
In what ways have computers changed learning environments?
How has learning improved with interactive multimedia and similar technologies?
What are the psychological perspectives on IT advancements?
What is the balance between high engagement and addiction to video games?
How has the video gaming industry changed over the decades?
Has social media helped or damaged our communication habits?

Research Paper Topics in Computer Science

What is the most important methodology in project planning?
How has technology improved people’s chances of winning in sports betting?
How has artificial technology impacted the U.S. economy?
What are the most effective project management processes in IT?
How can IT security systems help the practice of fraud score generation?
Has technology had an impact on religion?
How important is it to keep your social networking profiles up to date?

Computer Science Thesis Topics for College Students

How can logic and sets be used in computing?
How has online gambling impacted in-person gambling?
How did the 5-G network generation change communication?
What are the biggest challenges to IT due to Covid-19?
Do you agree that assembly language is a new way to determine data-mine health?
How can computer technology help track down criminals?
Is facial recognition software a violation of privacy rights?

Quick and Easy Computer Science Project Topics

Why do boys and girls learn the technology so differently?
How effective are computer training classes that target young girls?
How does technology affect how medicines are administered?
Will further advancements in technology put people out of work?
How has computer science changed the way teachers educate?
Which are the most effective ways of fighting identify theft?

Excellent Computer Science Thesis Topic Ideas

What are the foreseeable business needs computers will fix?
What are the pros and cons of having smart home technology?
How does computer modernization at the office affect productivity?
How has computer technology led to more job outsourcing?
Do self-service customer centers sufficiently provide solutions?
How can a small business compete without updated computer products?

Computer Science Presentation Topics

What does the future hold for virtual reality?
What are the latest innovations in computer science?
What are the pros and cons of automating everyday life?
Are hackers a real threat to our privacy or just to businesses?
What are the five most effective ways of storing personal data?
What are the most important fundamentals of software engineering?

Even More Topics in Computer Science

In what ways do computers function differently from human brains?
Can world problems be solved through advancements in video game technology?
How has computing helped with the mapping of the human genome?
What are the pros and cons of developing self-operating vehicles?
How has computer science helped developed genetically modified foods?
How are computers used in the field of reproductive technologies?

Our team of academic experts works around the clock to bring you the best project topics for computer science student. We search hundreds of online articles, check discussion boards, and read through a countless number of reports to ensure our computer science topics are up-to-date and represent the latest issues in the field. If you need assistance developing research topics in computer science or need help editing or writing your assignment, we are available to lend a hand all year. Just send us a message “ help me write my thesis ” and we’ll put you in contact with an academic writer in the field.

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Home > Engineering > Computer Science > Computer Science Graduate Projects

Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

High-Performance Domain-Specific Library for Hydrologic Data Processing , Kalyan Bhetwal

Verifying Data Provenance During Workflow Execution for Scientific Reproducibility , Rizbanul Hasan

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent , Ahmad Hojatimalekshah

Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design , Hailee Kiesecker

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation , Aaron St. George

Severity Measures for Assessing Error in Automatic Speech Recognition , Ryan Whetten

Theses/Dissertations from 2022 2022

Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design , James D. Beck

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

Long-Term Trends in Extreme Environmental Events with Changepoint Detection , Mintaek Lee

Structure Aware Smart Encoding and Decoding of Information in DNA , Shoshanna Llewellyn

Towards Making Transformer-Based Language Models Learn How Children Learn , Yousra Mahdy

Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems , Ramesh Neupane

Improving Children's Authentication Practices with Respect to Graphical Authentication Mechanism , Dhanush Kumar Ratakonda

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

Characterization and Mitigation of False Information on the Web , Anu Shrestha

Sinusoidal Projection for 360° Image Compression and Triangular Discrete Cosine Transform Impact in the JPEG Pipeline , Iker Vazquez Lopez

Theses/Dissertations from 2021 2021

Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom , Garrett Allen

Fair and Efficient Consensus Protocols for Secure Blockchain Applications , Golam Dastoger Bashar

Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior , Michael Green

Ensuring Consistency and Efficiency of the Incremental Unit Network in a Distributed Architecture , Mir Tahsin Imtiaz

Modeling Real and Fake News Sharing in Social Networks , Abishai Joy

Modeling and Analyzing Users' Privacy Disclosure Behavior to Generate Personalized Privacy Policies , A.K.M. Nuhil Mehdy

Into the Unknown: Exploration of Search Engines' Responses to Users with Depression and Anxiety , Ashlee Milton

Generating Test Inputs from String Constraints with an Automata-Based Solver , Marlin Roberts

A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework , Ravi Shankar

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

Towards Unifying Grounded and Distributional Semantics Using the Words-as-Classifiers Model of Lexical Semantics , Stacy Black

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow , Michael Burke

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

A Collection of Fast Algorithms for Scalar and Vector-Valued Data on Irregular Domains: Spherical Harmonic Analysis, Divergence-Free/Curl-Free Radial Basis Functions, and Implicit Surface Reconstruction , Kathryn Primrose Drake

Privacy-Preserving Protocol for Atomic Swap Between Blockchains , Kiran Gurung

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

Do You Feel Me?: Learning Language from Humans with Robot Emotional Displays , David McNeill

Obtaining Real-World Benchmark Programs from Open-Source Repositories Through Abstract-Semantics Preserving Transformations , Maria Anne Rachel Paquin

Content Based Image Retrieval (CBIR) for Brand Logos , Enjal Parajuli

A Resilience Metric for Modern Power Distribution Systems , Tyler Bennett Phillips

Theses/Dissertations from 2019 2019

Edge-Assisted Workload-Aware Image Processing System , Anil Acharya

MINOS: Unsupervised Netflow-Based Detection of Infected and Attacked Hosts, and Attack Time in Large Networks , Mousume Bhowmick

Deviant: A Mutation Testing Tool for Solidity Smart Contracts , Patrick Chapman

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

Suitability of Finite State Automata to Model String Constraints in Probablistic Symbolic Execution , Andrew Harris

UNICORN Framework: A User-Centric Approach Toward Formal Verification of Privacy Norms , Rezvan Joshaghani

Detection and Countermeasure of Saturation Attacks in Software-Defined Networks , Samer Yousef Khamaiseh

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy , Manish Kumar

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks , Muhammad Abu Naser Rony Chowdhury

Investigating the Effects of Social and Temporal Dynamics in Fitness Games on Children's Physical Activity , Ankita Samariya

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

FALCON: Framework for Anomaly Detection In Industrial Control Systems , Subin Sapkota

Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks , Samuel Ward Schrader

Incremental Processing for Improving Conversational Grounding in a Chatbot , Aprajita Shukla

Estimating Error and Bias of Offline Recommender System Evaluation Results , Mucun Tian

Theses/Dissertations from 2018 2018

Leveraging Tiled Display for Big Data Visualization Using D3.js , Ujjwal Acharya

Fostering the Retrieval of Suitable Web Resources in Response to Children's Educational Search Tasks , Oghenemaro Deborah Anuyah

Privacy-Preserving Genomic Data Publishing via Differential Privacy , Tanya Khatri

Injecting Control Commands Through Sensory Channel: Attack and Defense , Farhad Rasapour

Strong Mutation-Based Test Generation of XACML Policies , Roshan Shrestha

Performance, Scalability, and Robustness in Distributed File Tree Copy , Christopher Robert Sutton

Using DNA For Data Storage: Encoding and Decoding Algorithm Development , Kelsey Suyehira

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments , Kiran Thapa

Theses/Dissertations from 2017 2017

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews , Haritha Akella

Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia , Harish Arelli

CloudSkulk: Design of a Nested Virtual Machine Based Rootkit-in-the-Middle Attack , Joseph Anthony Connelly

Predicting Friendship Strength in Facebook , Nitish Dhakal

Privacy-Preserving Trajectory Data Publishing via Differential Privacy , Ishita Dwivedi

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment , Bret Allen Finley

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin , Joshua Holmes

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases , Akshay Kansal

Integrity Coded Databases: Ensuring Correctness and Freshness of Outsourced Databases , Ujwal Karki

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display , Yuan Li

The Effects of Pair-Programming in a High School Introductory Computer Science Class , Ken Manship

Towards Automatic Repair of XACML Policies , Shuai Peng

Identification of Unknown Landscape Types Using CNN Transfer Learning , Ashish Sharma

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

Learning to Code Music : Development of a Supplemental Unit for High School Computer Science , Kelsey Wright

Theses/Dissertations from 2016 2016

Identification of Small Endogenous Viral Elements within Host Genomes , Edward C. Davis Jr.

When the System Becomes Your Personal Docent: Curated Book Recommendations , Nevena Dragovic

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation , Jesse Lovitt

Towards Multipurpose Readability Assessment , Ion Madrazo

Evaluation of Topic Models for Content-Based Popularity Prediction on Social Microblogs , Axel Magnuson

CEST: City Event Summarization using Twitter , Deepa Mallela

Developing an ABAC-Based Grant Proposal Workflow Management System , Milson Munakami

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms , Anup Shrestha

A Certificateless One-Way Group Key Agreement Protocol for Point-to-Point Email Encryption , Srisarguru Sridhar

Dynamic Machine Level Resource Allocation to Improve Tasking Performance Across Multiple Processes , Richard Walter Thatcher

Theses/Dissertations from 2015 2015

Developing an Application for Evolutionary Search for Computational Models of Cellular Development , Nicolas Scott Cornia

Accelerated Radar Signal Processing in Large Geophysical Datasets , Ravi Preesha Geetha

Integrity Coded Databases (ICDB) – Protecting Integrity for Outsourced Databases , Archana Nanjundarao

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: CompSci Research Topics

Algorithms & data structures
Artificial intelligence ( AI )
Computer networking
Database systems
Human-computer interaction
Information security (IS)
Software engineering
Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
An evaluation of machine learning algorithms used for recommender systems in streaming services
A review of approximation algorithm approaches for solving NP-hard problems
An analysis of parallel algorithms for high-performance computing of genomic data
The influence of data structures on optimal algorithm design and performance in Fintech
A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
A comparison of streaming algorithm performance for the detection of elephant flows
A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
Exploring the feasibility of applying information theory to feature extraction in retail datasets.
Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

Applying deep learning algorithms for speech recognition in speech-impaired children
A review of the impact of artificial intelligence on decision-making processes in stock valuation
An evaluation of reinforcement learning algorithms used in the production of video games
An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
An analysis of the ethical and social implications of artificial intelligence-based automated marking
The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
An examination of the use of artificial intelligence in orthopaedic surgery
The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

An analysis of the impact of 5G technology on internet penetration in rural Tanzania
Assessing the role of software-defined networking (SDN) in modern cloud-based computing
A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
Exploring the influence of cloud computing on security risks in fintech.
An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
An evaluation of the challenges and opportunities in 6G wireless network adoption
The role of network congestion control algorithms in improving network performance on streaming platforms
An analysis of network coding-based approaches for data security
Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

An analysis of big data management systems and technologies used in B2B marketing
The impact of NoSQL databases on data management and analysis in smart cities
An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
Exploring the role of data warehousing and business intelligence in global consultancies
An analysis of the use of graph databases for data modelling and analysis in recommendation systems
The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
An examination of the challenges and opportunities of distributed databases in supply chain management
Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
An exploration of how artificial intelligence is changing human-computer interaction patterns in children
An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
Assessing the influence of virtual and augmented reality on consumer purchasing patterns
An examination of the use of gesture-based interfaces in architecture
Exploring the impact of ease of use in wearable technology on geriatric user
Evaluating the ramifications of gamification in the Metaverse
A systematic review of user experience (UX) design advances associated with Augmented Reality
A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

A bibliometric review of current trends in cryptography for secure communication
An analysis of secure multi-party computation protocols and their applications in cloud-based computing
An investigation of the security of blockchain technology in patient health record tracking
A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
A systematic review of secure data storage solutions used for cloud computing in the fintech industry
An analysis of intrusion detection and prevention systems used in the healthcare sector
Assessing security best practices for IoT devices in political offices
An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
A comparative study of digital signature schemes adoption in property transfers
An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

A study of agile software development methodologies and their impact on project success in pharmacology
Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
A study of the impact of DevOps practices on software development and delivery in the healthcare sector
An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
An investigation of software testing techniques and methodologies for subscription-based offerings
A review of software security practices and techniques for protecting against phishing attacks from social media
An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
Exploring the impact of software development outsourcing on project success in multinational contexts
An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

An array-based optimization framework for query processing and data analytics (Chen, 2021)
Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
Embedding constructural documentation in unit tests (Nassif, 2019)
PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
A state-based approach to context modeling and computing (Yue, 2019)
A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Research topics and ideas about data science and big data analytics

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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100 Great Computer Science Research Topics Ideas for 2023

Being a computer student in 2023 is not easy. Besides studying a constantly evolving subject, you have to come up with great computer science research topics at some point in your academic life. If you’re reading this article, you’re among many other students that have also come to this realization.

Interesting Computer Science Topics

Awesome research topics in computer science, hot topics in computer science, topics to publish a journal on computer science.

Controversial Topics in Computer Science

Fun AP Computer Science Topics

Exciting computer science ph.d. topics, remarkable computer science research topics for undergraduates, incredible final year computer science project topics, advanced computer science topics, unique seminars topics for computer science, exceptional computer science masters thesis topics, outstanding computer science presentation topics.

Key Computer Science Essay Topics

Main Project Topics for Computer Science

We Can Help You with Computer Science Topics

Whether you’re earnestly searching for a topic or stumbled onto this article by accident, there is no doubt that every student needs excellent computer science-related topics for their paper. A good topic will not only give your essay or research a good direction but will also make it easy to come up with supporting points. Your topic should show all your strengths as well.

Fortunately, this article is for every student that finds it hard to generate a suitable computer science topic. The following 100+ topics will help give you some inspiration when creating your topics. Let’s get into it.

One of the best ways of making your research paper interesting is by coming up with relevant topics in computer science . Here are some topics that will make your paper immersive:

Evolution of virtual reality
What is green cloud computing
Ways of creating a Hopefield neural network in C++
Developments in graphic systems in computers
The five principal fields in robotics
Developments and applications of nanotechnology
Differences between computer science and applied computing

Your next research topic in computer science shouldn’t be tough to find once you’ve read this section. If you’re looking for simple final year project topics in computer science, you can find some below.

Applications of the blockchain technology in the banking industry
Computational thinking and how it influences science
Ways of terminating phishing
Uses of artificial intelligence in cyber security
Define the concepts of a smart city
Applications of the Internet of Things
Discuss the applications of the face detection application

Whenever a topic is described as “hot,” it means that it is a trendy topic in computer science. If computer science project topics for your final years are what you’re looking for, have a look at some below:

Applications of the Metaverse in the world today
Discuss the challenges of machine learning
Advantages of artificial intelligence
Applications of nanotechnology in the paints industry
What is quantum computing?
Discuss the languages of parallel computing
What are the applications of computer-assisted studies?

Perhaps you’d like to write a paper that will get published in a journal. If you’re searching for the best project topics for computer science students that will stand out in a journal, check below:

Developments in human-computer interaction
Applications of computer science in medicine
Developments in artificial intelligence in image processing
Discuss cryptography and its applications
Discuss methods of ransomware prevention
Applications of Big Data in the banking industry
Challenges of cloud storage services in 2023

Controversial Topics in Computer Science

Some of the best computer science final year project topics are those that elicit debates or require you to take a stand. You can find such topics listed below for your inspiration:

Can robots be too intelligent?
Should the dark web be shut down?
Should your data be sold to corporations?
Will robots completely replace the human workforce one day?
How safe is the Metaverse for children?
Will artificial intelligence replace actors in Hollywood?
Are social media platforms safe anymore?

Are you a computer science student looking for AP topics? You’re in luck because the following final year project topics for computer science are suitable for you.

Standard browser core with CSS support
Applications of the Gaussian method in C++ development in integrating functions
Vital conditions of reducing risk through the Newton method
How to reinforce machine learning algorithms.
How do artificial neural networks function?
Discuss the advancements in computer languages in machine learning
Use of artificial intelligence in automated cars

When studying to get your doctorate in computer science, you need clear and relevant topics that generate the reader’s interest. Here are some Ph.D. topics in computer science you might consider:

Developments in information technology
Is machine learning detrimental to the human workforce?
How to write an algorithm for deep learning
What is the future of 5G in wireless networks
Statistical data in Maths modules in Python
Data retention automation from a website using API
Application of modern programming languages

Looking for computer science topics for research is not easy for an undergraduate. Fortunately, these computer science project topics should make your research paper easy:

Ways of using artificial intelligence in real estate
Discuss reinforcement learning and its applications
Uses of Big Data in science and medicine
How to sort algorithms using Haskell
How to create 3D configurations for a website
Using inverse interpolation to solve non-linear equations
Explain the similarities between the Internet of Things and artificial intelligence

Your dissertation paper is one of the most crucial papers you’ll ever do in your final year. That’s why selecting the best ethics in computer science topics is a crucial part of your paper. Here are some project topics for the computer science final year.

How to incorporate numerical methods in programming
Applications of blockchain technology in cloud storage
How to come up with an automated attendance system
Using dynamic libraries for site development
How to create cubic splines
Applications of artificial intelligence in the stock market
Uses of quantum computing in financial modeling

Your instructor may want you to challenge yourself with an advanced science project. Thus, you may require computer science topics to learn and research. Here are some that may inspire you:

Discuss the best cryptographic protocols
Advancement of artificial intelligence used in smartphones
Briefly discuss the types of security software available
Application of liquid robots in 2023
How to use quantum computers to solve decoherence problem
macOS vs. Windows; discuss their similarities and differences
Explain the steps taken in a cyber security audit

When searching for computer science topics for a seminar, make sure they are based on current research or events. Below are some of the latest research topics in computer science:

How to reduce cyber-attacks in 2023
Steps followed in creating a network
Discuss the uses of data science
Discuss ways in which social robots improve human interactions
Differentiate between supervised and unsupervised machine learning
Applications of robotics in space exploration
The contrast between cyber-physical and sensor network systems

Are you looking for computer science thesis topics for your upcoming projects? The topics below are meant to help you write your best paper yet:

Applications of computer science in sports
Uses of computer technology in the electoral process
Using Fibonacci to solve the functions maximum and their implementations
Discuss the advantages of using open-source software
Expound on the advancement of computer graphics
Briefly discuss the uses of mesh generation in computational domains
How much data is generated from the internet of things?

A computer science presentation requires a topic relevant to current events. Whether your paper is an assignment or a dissertation, you can find your final year computer science project topics below:

Uses of adaptive learning in the financial industry
Applications of transitive closure on graph
Using RAD technology in developing software
Discuss how to create maximum flow in the network
How to design and implement functional mapping
Using artificial intelligence in courier tracking and deliveries
How to make an e-authentication system

Key Computer Science Essay Topics

You may be pressed for time and require computer science master thesis topics that are easy. Below are some topics that fit this description:

What are the uses of cloud computing in 2023
Discuss the server-side web technologies
Compare and contrast android and iOS
How to come up with a face detection algorithm
What is the future of NFTs
How to create an artificial intelligence shopping system
How to make a software piracy prevention algorithm

One major mistake students make when writing their papers is selecting topics unrelated to the study at hand. This, however, will not be an issue if you get topics related to computer science, such as the ones below:

Using blockchain to create a supply chain management system
How to protect a web app from malicious attacks
Uses of distributed information processing systems
Advancement of crowd communication software since COVID-19
Uses of artificial intelligence in online casinos
Discuss the pillars of math computations
Discuss the ethical concerns arising from data mining

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How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2024-2025

Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

Machine learning driven design
Generative models for structured discrete objects
Approximate inference in probabilistic models
Accelerating solutions to partial differential equations
Innovative uses of automatic differentiation
Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

Available for Fall 2024 IW advising, only

Research Areas: Formal methods, programming languages, compilers, computer security.
Software verification (for which taking COS 326 / COS 510 is helpful preparation)
Game theory of poker or other games (for which COS 217 / 226 are helpful)
Computer game-playing programs (for which COS 217 / 226)
Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487).
Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages.
Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
Any topic in theoretical computer science.

David August, Room 221

Not available for IW or thesis advising, 2024-2025

Research Areas: Computer Architecture, Compilers, Parallelism
Containment-based approaches to security: We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system. Let's explore ways to use containment to solve real problems. Expect to work with corporate security and technology decision-makers.
Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures. Let's find ways to realize this parallelism.
Any other interesting topic in computer architecture or compilers.

Mark Braverman, 194 Nassau St., Room 231

Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
Topics in computational and communication complexity.
Applications of information theory in complexity theory.
Algorithms for problems under real-life assumptions.
Game theory, network effects
Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
Methods for collaborative and continual learning.
Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms.
Natural algorithms (flocking, swarming, social networks, etc).
Sublinear algorithms
Self-improving algorithms
Markov data structures

Danqi Chen, Room 412

My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
Representation learning for text and knowledge bases
Pre-training and transfer learning
Question answering and reading comprehension
Information extraction
Text summarization
Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)
Research Areas: Machine learning

Jia Deng, Room 423

Research Areas: Computer Vision, Machine Learning.
Object recognition and action recognition
Deep Learning, autoML, meta-learning
Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

Research Areas: Software engineering; software engineering education.
Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
Using RL algorithms to applications in science and engineering.
Emergent behavior of RL algorithms on high-fidelity robotic simulators.
Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
Quantitative approaches to theoretical linguistics questions
Extensions and interfaces for WordNet (English and WN in other languages),
Applications of WordNet(s), including:
Foreign language tutoring systems,
Spelling correction software,
Word-finding/suggestion software for ordinary users and people with memory problems,
Machine Translation
Sentiment and Opinion detection
Automatic reasoning and inferencing
Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424

Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

Networking and telecommunications
Learning, perception, and intelligence, artificial and otherwise;
Human-computer interaction and computer-supported cooperative work
Online education, especially in Computer Science Education
Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308

Research Areas: Distributed systems, security, networking
Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
Develop a technique for understanding AI models
Design a AI model that is interpretable by design
Build a paradigm for detecting and/or correcting failure points in an AI model
Analyze an existing AI model and/or dataset to better understand its failure points
Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
Develop a software package for explainable AI
Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Available for Fall 2024 single-semester IW advising, only

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

Research Areas: Formal methods, program analysis, logic decision procedures
Finding bugs in open source software using automatic verification tools
Software verification (program analysis, model checking, test generation)
Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409

Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
Implementation and algorithm engineering for control, reinforcement learning and robotics
Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
Optical Neural Networks
Hardware-in-the-loop Holography
Zero-shot and Simulation-only Learning
Object recognition in extreme conditions
3D Scene Representations for View Generation and Inverse Problems
Long-range Imaging in Scattering Media
Hardware-in-the-loop Illumination and Sensor Optimization
Inverse Lidar Design
Phase Retrieval Algorithms
Proximal Algorithms for Learning and Inference
Domain-Specific Language for Optics Design

Peter Henderson , 302 Sherrerd Hall

Research Areas: Machine learning, law, and policy

Kyle Jamieson, Room 306

Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
See other topics on my independent work ideas page (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
Software visualization tools for education
Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
Application-oriented languages, scripting languages.
Tools; user interfaces
Digital humanities

Zachary Kincaid, Room 219

Research areas: programming languages, program analysis, program verification, automated reasoning
Independent Research Topics:
Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
Design a domain-specific programming language, or prototype a new feature for an existing language.
Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Research area: theory

Aleksandra Korolova, 309 Sherrerd Hall

Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Pravesh Kothari, Room 320

Research areas: Theory

Amit Levy, Room 307

Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
Distributed hardware testing infrastructure
Second factor security tokens
Low-power wireless network protocol implementation
USB device driver implementation

Kai Li, Room 321

Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
Fast communication mechanisms for heterogeneous clusters.
Approximate nearest-neighbor search for high dimensional data.
Data analysis and prediction of in-patient medical data.
Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
Explore new statistical retrieval models for document retrieval and question answering.
Apply AI in various fields.
Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
Any interesting project related to AI, machine learning, and data analysis.

Lydia Liu, Room 414

Research Areas: algorithmic decision making, machine learning and society
Theoretical foundations for algorithmic decision making (e.g. mathematical modeling of data-driven decision processes, societal level dynamics)
Societal impacts of algorithms and AI through a socio-technical lens (e.g. normative implications of worst case ML metrics, prediction and model arbitrariness)
Machine learning for social impact domains, especially education (e.g. responsible development and use of LLMs for education equity and access)
Evaluation of human-AI decision making using statistical methods (e.g. causal inference of long term impact)

Wyatt Lloyd, Room 323

Research areas: Distributed Systems
Caching algorithms and implementations
Storage systems
Distributed transaction algorithms and implementations

Alex Lombardi , Room 312

Research Areas: Theory

Margaret Martonosi, Room 208

Quantum Computing research, particularly related to architecture and compiler issues for QC.
Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307

Available for Spring 2025 single-semester IW, only

Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
Assessing the effects of government policies, both in the public and private sectors.
Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
Developing new tools to improve government processes and offer policy alternatives.

Mae Milano, Room 307

Local-first / peer-to-peer systems
Wide-ares storage systems
Consistency and protocol design
Type-safe concurrency
Language design
Gradual typing
Domain-specific languages
Languages for distributed systems

Andrés Monroy-Hernández, Room 405

Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
Research interests:developing public-interest socio-technical systems. We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems. We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

Research areas: Distributed systems, high-throughput computing, computer science/engineering education
Expansion, improvement, and evaluation of open-source distributed computing software.
Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

Research areas: Natural Language Processing, Reinforcement Learning
Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
Transfer learning/generalization in NLP
Techniques for generating natural language
Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

* Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in: * Explicit constructions of better vertex expanders and/or unique neighbor expanders. * Construction deterministic or random high dimensional expanders. * Pseudorandom generators for different problems. * Topics around the quantum PCP conjecture. * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

2. Theory informed practical implementations of algorithms Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest: * Streaming algorithms. * Numeric linear algebra. * Property testing. * Parallel / Distributed algorithms. * Online algorithms. 3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
Possible research topics include:

1. Quantum algorithms and circuits:

i. Design or simulation quantum circuits implementing quantum algorithms.
ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2. Information Based Complexity:

i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case).
ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
iii. Necessary and sufficient conditions for tractability of Weighted problems.
iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309

Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
Implementation and application of algorithms to infer evolutionary processes in cancer
Identifying correlations between combinations of genomic mutations in human and cancer genomes
Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
Constructing a new method to explain a model / create an interpretable by design model
Analyzing a current model / dataset to understand bias within the model/dataset
Proposing new fairness evaluations
Proposing new methods to train to improve fairness
Developing synthetic datasets for fairness / interpretability benchmarks
Understanding robustness of models

Ran Raz, Room 240

Research Area: Computational Complexity
Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
Investigate approaches to robust 3D scan matching
Model and compensate for imperfections in 3D printing
Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
Gamification of neuroscience (EyeWire 2.0)
Semantic segmentation and object detection in brain images from microscopy
Computational analysis of brain structure and function
Neural network theories of brain function

Jaswinder Pal Singh, Room 324

Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
Develop a startup company idea, and build a plan/prototype for it.
Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
Study and develop methods to infer insights from data in different application areas, from science to search to finance to others.
Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
Whole and cross-genome methods for predicting protein function and protein-protein interactions.
Analysis and prediction of biological networks.
Computational methods for inferring specific aspects of protein structure from protein sequence data.
Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
Implement and evaluate one or more gene expression analysis algorithm.
Develop algorithms for assessment of performance of genomic analysis methods.
Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
Independent Research Topics: Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

Available for Fall 2024 single-semester IW, only

Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
Design and implement computer visualizations of algorithms or data structures.
Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc.
Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

data structures
streaming algorithms
design and analyze data structures / streaming algorithms
prove impossibility results (lower bounds)
implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

Opportunities outside the department.

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science. However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.) To do a project with an adviser outside of computer science you must have permission of the department. This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

Documentation of historic structures
Cyber physical systems for structural health monitoring
Developing virtual and augmented reality applications for documenting structures
Applying machine learning techniques to generate 3D models from 2D plans of buildings
Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

Consumer protection
Content regulation
Competition law
Economic development
Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

Design of reliable hardware systems
Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

Internet security and privacy
Social Networks
Privacy technologies, anonymous communication
Network Science
Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman, Psychology Dept, PNI 137

Research Areas: Memory, the brain and computation
Lab: Princeton Computational Memory Lab

Potential research topics

Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG)
Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The Campus as Lab program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its website .

An example from Computer Science could include using TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a live energy heatmap of campus .

Other potential projects include:

Apply game theory to sustainability challenges
Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

Instrument Princeton's Green (HPCRC) data center
Investigate power utilization on an processor core implemented in an FPGA
Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
Other topics in parallel computer architecture or operating systems

Thesis Helpers

Find the best tips and advice to improve your writing. Or, have a top expert write your paper.

105 Computer Science Thesis Topics And Writing Guide

After years of hard work and struggle, you have reached the most important stage of your academic life – writing a computer science thesis. It is clear that this is a defining moment for your life, and you wouldn’t want to throw anything to chance.

As a rule, most of the computer science theses consist of two major parts – writing a particular program and writing a paper, describing its functionality and the reasons behind its development.

Whether yours is a computer science bachelor thesis, computer science Master’s thesis, or a computer science Ph.D. thesis, you know it is not easy. With all the algorithms, binary equations, and programming calculations in your head, you might end up breaking down.

That calls for computer science help. Please wait a moment; I know what you are thinking. ‘Can I trust computer science help online for such a technical subject?’ Yes, you can! As you delve deep, you will realize why we are the best match for this kind of task.

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Computer Science Thesis Outline: How To Structure It?

Like any other academic research paper, a computer science thesis has a well-laid out structure that you will follow. An outline helps underpin the bulk of such a demanding paper into manageable parts.

Before we delve into this, we have to understand that there are various computer science fields such as:

Computer hardware systems Software systems Database systems Discrete mathematics Scientific computing

Therefore, whenever you determine the outline of your bachelor’s or master thesis computer science outline, bear those categories in mind. They will help you narrow down your research to a specific area, thus saving you time and energy too!

Depending on your institution, you will have a specified outline for your computer science thesis. However, the following parts form the standard outline of any thesis paper for Masters or Ph.D.

Introduction (contains the background, statement of the problem, research questions, hypothesis, etc.)
Next is the literature review , which gives the theoretical framework of your research
The methodology section describes and justifies the methods to be used in data collection.
The results and discussion section gives answers to your research questions and explains their meaning.
Finally, the conclusion and recommendations review the findings and results and give a summary of the research.

The outline of a Master’s thesis in Computer Science or Ph. D. might vary depending on its requirements. Be sure to confirm with your professor on which outline to follow.

Master Thesis Computer Science Writing Guide

Writing such a large scale project is not something that can be done in a few days. The technicality of the computer science field makes it all the more complex. Some will tell you that it is more technical than coming up with a computer program.

That said, here are professionally handpicked tips for your Master Thesis in Computer Science:

Understand the purpose of your computer thesis If you are writing on a computer program, show a deeper knowledge and understanding of the unique and fresh program. Let the reader see that you have mastered your program to the core.
Begin writing early It is not something you can plan to do on the eve of the submission date. It will make your writing process light and highly motivational, especially with the voluminous books you will have to read.
Selecting your topic It is a task that sends chills down the spine of many students. Thus, your topic should not be too narrow or too broad – this will portray you as an amateur. Draw your computer science thesis topic from something you encountered during your coursework.
Keep reading To be precise, read a little, write a little, every day. It will surprise you how much ground you will have covered by the time you are submitting your thesis for review.

Latest Computer Science Thesis Topics For You!

Below is a comprehensive list of original computer science thesis topics for your inspiration:

A case study of the pitfalls of assembly languages used to develop applications, websites, and software.
Design and development of artificial intelligence systems
Process improvement techniques for the functionality of robots
An analysis of the factors that necessitate Java as one of the best programming languages
What is the place of ethical hacking in today’s digital society?
How to improve human-computer interaction
What is the potential of computer systems in combating terrorism and crime?
Identify how cyber-security enhances data confidentiality
The design and engineering of computer applications and other systems
Highlighting the differences between programming languages
How can organizations make use of data mining?
Identify efficient logistics in software architecture
The effect of globalization and its impact on database administration
A detailed investigation into the data availability and security
The influence and impact of emerging computer technologies on the healthcare system
Effect of training on knowledge performance in computer performance optimization
The behavior of network architecture within a computing environment
How can learning institutions implement computer systems for virtual and distance learning?
Why risk management is necessary for data protection and information security in companies
A detailed review of the role of education and industrialization on the development of computer systems.

From these topics, you can derive more computer science thesis topics for your presentation. Remember that the topic should be on a subject or field that is of interest to you. Settling on a complex and least researched topic might not be a good idea for you.

We can help you unearth more topics for your thesis paper. Read to the end to find out how?

Computer Science Thesis Topics

You need a computer science thesis topic before getting a computer science degree. Here is a list of interesting topics to create the best essay yet:

Explain how to Blockchain benefits big businesses
Discuss the conversation on using pet tracking chips
Examine how genetic sequencing works using a computer
How does IT help with security in financial institutions?
Discuss what digitization means for privacy in the medical field
What are the most effective ways to backup data in the medical field?
Discuss the limitations of communication and computation
Would you say the average ATM is secure?
Analyze an innovation that seems threatening but seems to be a favorite for the human race
Why should any business utilize open-source software, and how does it help with security?
Discuss the role of technology in the classroom
Discuss the personal or home use of quantum computers
Would you say embedded systems are changing how the world works?
What would you say about social media and technology trends
Will technology reduce recruitment in an industry (of your choosing)
How does technology affect human interactions, and will AI remedy that?
Examine the computer assistance that can help businesses perform efficient customer care
Analyze the technologies involved in casino live gambling
Would you say artificial intelligence is a threat or blessing to contemporary society
Explain if machine learning impact neurons and the way the brain works negatively
How does Big Data help corporations
Examine the average human’s knowledge of virtual reality through quantitative research
Philosophize the future of technology
Examine the future of programming languages and their efficiency
What is the most creative development in computer technology yet?

Computer Science Topics

If you are fascinated by computer science and technology, you may want to conduct in-depth research into several fields. Here are interesting topics for a computer science thesis to review:

Discuss databases, data mining, and how cryptocurrency works
Examine the network between neuron network and machine learning
How do robots and computers understand human language
Examine the role of mathematics in modeling computers
Discuss encryption and decryption of data
How does computer-aided learning work
How can you achieve usability in human-computer interaction
Are there any hacking ethics?
Discuss the advantages and disadvantages of the cloud storage
What are the cybersecurity threats in banking systems
Are there any loopholes in the technology of DAOs
How does Blockchain Technology help the world
Discuss the role of wireless systems in vehicles
Examine how biometric systems work in cars
Analyze how cryptography works.

Computer Science Research Topics

If you’ve ever wondered how technology and the world cooperate, here are some of the best topics to research and provide answers to for your essay or paper.

What is the nexus of technology and finance?
Examine the relationship between technology and healthcare
Can robots work without any human intervention?
How do computers interfere with forensics?
Discuss computer security and information and how they work
Examine the concerns of privacy in electronic health
What are the vulnerabilities in bioinformatics?
Explain the buildup of cyber-physical systems
How has deep learning helped an industry of your choice
What are the process computer take to analyze language
Discuss the basic techniques for computer security
Examine how natural language processing works
Give an overview of textual mining
How do deep visual models work?
What is meant by distributed data clustering?

Research Topics in Computer Science

Computer science deals with how computer systems work. It is all about computer programs, strategies for development, and how they help humans. Here are 15 topics for you:

Examine the role of computer technology in sports
How does technology boost the performance of professional athletes?
Would you say technology can lead to addiction?
Explore the strategies used in gaming technology
How does computer technology influence management solutions?
Discuss the role of technology in the engineering field
What is the future of information technology?
What are the key developments and trends that show the vulnerabilities of technology?
Using Tesla as a case study, what are technology’s vulnerabilities in automobile manufacturing and development.
What does psychology say about the different Advancements in technology
Examine the evolution of the gaming industry and how it has changed the perception of entertainment
Track the evolution of the entertainment industry and technology has helped propel it
What are the ways technology has propelled interactive media?
Discuss the ways technology has influenced sports betting
How has technology helped with fraud detection in the finance sector?

Computer Science Topics for Research

Being a computer science student means brainstorming, researching, and giving in-depth interpretations of why and how some things happen. Here are some relevant computer science research topics to steer your critical thinking skills:

Analyze technological innovations in the construction and real estate industries
Can AI have an impact on the economy of any country?
Technology has helped with the way we understand the environment: argue
Can technology help with how we solve the climate crisis?
What is the role of technology in social media marketing?
Discuss the technology companies like Google use to offer internet services
Deeply analyze why some Mobile phones cannot work in the US
How does the technology work in the creation of smart home systems?
Data mining and ethical concerns: what are they?
Will the 5-G network change how phones connect?
Discuss the most important technology trends since COVID-19
Why is facial scanning vulnerable to privacy breaches?
How can computer technology help in tracking crime and offenders?
What is the core buildup of math computing?
Explain the most effective and ethical ways to tackle identity theft.

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Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Eﬃcient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters , Adel Alshehri

Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari

Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee

A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao

Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin

Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano

Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang

A GPU-Based Framework for Parallel Spatial Indexing and Query Processing , Zhila Nouri Lewis

A Flexible, Natural Deduction, Automated Reasoner for Quick Deployment of Non-Classical Logic , Trisha Mukhopadhyay

An Efficient Run-time CFI Check for Embedded Processors to Detect and Prevent Control Flow Based Attacks , Srivarsha Polnati

Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain , John Rippetoe

Detecting Digitally Forged Faces in Online Videos , Neilesh Sambhu

Malicious Manipulation in Service-Oriented Network, Software, and Mobile Systems: Threats and Defenses , Dakun Shen

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Senior Thesis

A senior thesis is more than a big project write-up. It is documentation of an attempt to contribute to the general understanding of some problem of computer science, together with exposition that sets the work in the context of what has come before and what might follow. In computer science, some theses involve building systems, some involve experiments and measurements, some are theoretical, some involve human subjects, and some do more than one of these things. Computer science is unusual among scientific disciplines in that current faculty research has many loose ends appropriate for undergraduate research.

Senior thesis projects generally emerge from collaboration with faculty. Students looking for senior thesis projects should tell professors they know, especially professors whose courses they are taking or have taken, that they are looking for things to work on. See the page on CS Research for Undergrads . Ideas often emerge from recent papers discussed in advanced courses. The terms in which some published research was undertaken might be generalized, relaxed, restricted, or applied in a different domain to see if changed assumptions result in a changed solution. Once a project gets going, it often seems to assume a life of its own.

To write a thesis, students may enroll in Computer Science 91r one or both terms during their senior year, under the supervision of their research advisor. Rising seniors may wish to begin thinking about theses over the previous summer, and therefore may want to begin their conversations with faculty during their junior spring—or even try to stay in Cambridge to do summer research.

An information session for those interested in writing a senior thesis is held towards the end of each spring semester. Details about the session will be posted to the [email protected] email list.

Students interested in commercializing ideas in their theses may wish to consult Executive Dean Fawwaz Habbal about patent protection. See Harvard’s policy for information about ownership of software written as part of your academic work.

Thesis Supervisor

You need a thesis supervisor. Normally this is a Harvard Computer Science faculty member. Joint concentrators (and, in some cases, non-joint concentrators) might have a FAS/SEAS Faculty member from a different field as their thesis supervisor. Exceptions to the requirement that the thesis supervisor is a CS or FAS/SEAS faculty member must be approved by the Director of Undergraduate Studies. For students whose advisor is not a Harvard CS faculty member, note that at least one of your thesis readers must be a Harvard CS faculty member, and we encourage you to talk with this faculty member regularly to help ensure that your thesis is appropriately relevant for Harvard Computer Science.

It’s up to you and your supervisor how frequently you meet and how engaged the supervisor is in your thesis research. However, we encourage you to meet with your supervisor at least several times during the Fall and Spring, and to agree on deadlines for initial results, chapter outlines, drafts, etc.

Thesis Readers

The thesis is evaluated by the thesis readers. Thesis readers must be either:

Two Harvard CS faculty members/affiliates ; or:

Three readers, at least one of whom is a Harvard CS faculty member and the others are ordinarily teaching faculty members of the Faculty of Arts and Sciences or SEAS who are generally familiar with the research area.

The thesis supervisor is one of the readers.

The student is responsible for finding the other readers, but you can talk with your supervisor for suggestions of possible readers.

Exceptions to these thesis reader requirements must be approved by the Directors of Undergraduate Studies.

For joint concentrators, the other concentration may have different procedures for thesis readers; if you have any questions or concerns about thesis readers, please contact the Directors of Undergraduate Studies.

Senior Thesis Seminar

Computer Science does not have a Senior Thesis seminar course.

However, we do run an informal optional series of Senior Thesis meetings in the Fall to help with the thesis writing process, focused on topics such as technical writing tips, work-shopping your senior thesis story, structure of your thesis, and more. Pay attention to your email in the Fall for announcements about this series of meetings.

The thesis should contain an informative abstract separate from the body of the thesis. This abstract should clearly state what the contribution of the thesis is–which parts are expository, whether there are novel results, etc. We also recommend the thesis contain an introduction that is at most 5 pages in length that contains an “Our contributions” section which explains exactly what the thesis contributed, and which sections in the thesis these are elaborated on. At the degree meeting, the Committee on Undergraduate Studies in Computer Science will review the thesis abstract, the reports from the three readers and the student’s academic record; it will have access to the thesis. The readers (and student) are told to assume that the Committee consists of technical professionals who are not necessarily conversant with the subject matter of the thesis so their reports (and abstract) should reflect this audience.

The length of the thesis should be as long as it needs to be to present its arguments, but no longer!

There are no specific formatting guidelines. For LaTeX, some students have used this template in the past . It is set up to meet the Harvard PhD Dissertation requirements, so it is meeting requirements that you as CS Senior Thesis writers don’t have.

Thesis Timeline for Seniors

(The timeline below is for students graduating in May. For off-cycle students, the same timeline applies, but offset by one semester. The thesis due date for March 2025 graduates is Friday November 22, 2024 at 2pm. The thesis deadline for May 2024 graduates is Friday March 29th Monday April 1st at 2pm.

Please be aware that students writing a joint thesis must meet the requirements of both departments–so if there are two different due dates for the thesis, you are expected to meet the earlier date.

Senior Fall (or earlier) Find a thesis supervisor, and start research.

October/November/December Start writing.

All fourth year concentrators are contacted by the Office of Academic Programs and those planning to submit a senior thesis are requested to supply certain information, including name of advisor and a tentative thesis title. You may use a different title when you submit your thesis; you do not need to tell us your updated title before then. If Fall 2024 is your final term, please fill out this form . If May 2024 is your final term, please fill out this form .

Early February The student should provide the name and contact information for the readers (see above), together with assurance that they have agreed to serve.

Mid-March Thesis supervisors are advised to demand a first draft. (A common reaction of thesis readers is “This would have been an excellent first draft. Too bad it is the final thesis—it could have been so much better if I had been able to make some suggestions a couple of weeks ago.")

April 1, 2024 * Thesis is due by 2:00 pm. Electronic copies in PDF format should be delivered by the student to all three readers and to [email protected] (which will forward to the Director of Undergraduate Studies) on or before that date. An electronic copy should also be submitted via the SEAS online submission tool on or before that date. SEAS will keep this electronic copy as a non-circulating backup. During this online submission process, the student will also have the option to make the electronic copy publicly available via DASH, Harvard’s open-access repository for scholarly work. Please note that the thesis will NOT be published to ProQuest. More information can be found on the SEAS Senior Thesis Submission page.

The two or three readers will receive a rating sheet to be returned to the Office of Academic Programs before the beginning of the Reading Period, together with their copy of the thesis and any remarks to be transmitted to the student.

Late May The Office of Academic Programs will send students their comments after the degree meeting to decide honors recommendations.

Thesis Extensions and Late Submissions

Thesis extensions Thesis extensions will be granted in extraordinary circumstances, such as hospitalization or grave family emergency, with the support of the thesis advisor and resident dean and the agreement of all readers. For joint concentrators, the other concentration should also support the extension. To request an extension, please have your advisor or resident dean email [email protected] , ideally several business days in advance, so that we may follow up with readers. Please note that any extension must be able to fall within our normal grading, feedback, and degree recommendation deadline, so extensions of more than a few days are usually impossible.

Late submissions Late submission of thesis work should be avoided. Work that is late will ordinarily not be eligible for thesis prizes like the Hoopes Prize. Theses submitted late will ordinarily be penalized one full level of honors (highest honors, high honors, honors, no honors) per day late or part thereof, including weekends, so a thesis submitted two days and one minute late is ordinarily ineligible to receive honors. Penalties will be waived only in extraordinary cases, such as documented medical illness or grave family emergency; students should consult with the Directors of Undergraduate Studies in that event. Missed alarm clocks, crashed computers, slow printers, corrupted files, and paper jams are not considered valid causes for extensions.

Thesis Examples

Recent thesis examples can be found on the Harvard DASH (Digital Access to Scholarship at Harvard) repository here . Examples of Mind, Brain, Behavior theses are here .

Spectral Sparsification: The Barrier Method and its Applications

Martin Camacho, Advisor: Jelani Nelson

Good Advice Costs Nothing and it’s Worth the Price: Incentive Compatible Recommendation Mechanisms for Exploring Unknown Options

Perry Green, Advisor: Yiling Chen

Better than PageRank: Hitting Time as a Reputation Mechanism

Brandon Liu, Advisor: David Parkes

Tree adjoining grammar at the interfaces

Nicholas Longenbaugh, Advisor: Stuart Shieber

SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder

Dylan Nagler, Advisor: Ryan Adams

Learning over Molecules: Representations and Kernels

Jimmy Sun, Advisor: Ryan Adams

Towards the Quantum Machine: Using Scalable Machine Learning Methods to Predict Photovoltaic Efficacy of Organic Molecules

Michael Tingley, Advisor: Ryan Adams

The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an introduction , which presents a brief overview of the topic and the research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging: A deep learning approach to improve the accuracy of medical diagnoses.

Introduction: Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction: Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction: Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction: Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction: Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by Joanna Kosinska on Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction: Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction: Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction: Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction: Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction: Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by UX Indonesia on Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction: Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction: Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction: Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction: Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction: Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by Windows on Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction: Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction: Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction: Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction: Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction: Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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Home > School, College, or Department > MCECS > Computer Science > Dissertations and Theses

Computer Science Dissertations and Theses

Theses/dissertations from 2024 2024.

MmWave RAT Optimization: MAC Layer Initial Access Design and Transport Layer Integration , Suresh Srinivasan (Dissertation)

Theses/Dissertations from 2023 2023

Seeing in the Dark: Towards Robust Pedestrian Detection at Nighttime , Afnan Althoupety (Dissertation)

A Deep Hierarchical Variational Autoencoder for World Models in Complex Reinforcement Learning Environments , Sriharshitha Ayyalasomayajula (Thesis)

Toward Efficient Rendering: A Neural Network Approach , Qiqi Hou (Dissertation)

Energy Auction with Non-Relational Persistence , Michael Ramez Howard (Thesis)

Implementing a Functional Logic Programming Language via the Fair Scheme , Andrew Michael Jost (Dissertation)

Multi-Agent Deep Reinforcement Learning for Radiation Localization , Benjamin Scott Totten (Thesis)

Theses/Dissertations from 2022 2022

Using Intrinsically-Typed Definitional Interpreters to Verify Compiler Optimizations in a Monadic Intermediate Language , Dani Barrack (Thesis)

An Automated Zoom Class Session Analysis Tool to Improve Education , Jack Arlo Cannon II (Thesis)

Scaling EPA-RIMM with Multicore System Management Interrupt Handlers , Alexander K. Freed (Thesis)

Unpaired Style Transfer Conditional Generative Adversarial Network for Scanned Document Generation , David Jonathan Hawbaker (Thesis)

Toward Analyzing the Diversity of Extractive Summaries , Aaron David Hudson (Thesis)

Making Curry with Rice: An Optimizing Curry Compiler , Steven Libby (Dissertation)

Domain Knowledge as Motion-Aware Inductive Bias for Deep Video Synthesis: Two Case Studies , Long Mai (Dissertation)

Theses/Dissertations from 2021 2021

Efficient Neuromorphic Algorithms for Gamma-Ray Spectrum Denoising and Radionuclide Identification , Merlin Phillip Carson (Thesis)

Storing Intermediate Results in Space and Time: SQL Graphs and Block Referencing , Basem Ibrahim Elazzabi (Dissertation)

Automated Test Generation for Validating SystemC Designs , Bin Lin (Dissertation)

Forecasting Optimal Parameters of the Broken Wing Butterfly Option Strategy Using Differential Evolution , David Munoz Constantine (Thesis)

Situate: An Agent-Based System for Situation Recognition , Max Henry Quinn (Dissertation)

Theses/Dissertations from 2020 2020

Multiple Diagram Navigation , Hisham Benotman (Dissertation)

Smart Contract Vulnerabilities on the Ethereum Blockchain: a Current Perspective , Daniel Steven Connelly (Thesis)

Extensible Performance-Aware Runtime Integrity Measurement , Brian G. Delgado (Dissertation)

Novel View Synthesis - a Neural Network Approach , Hoang Le (Dissertation)

Exploring the Potential of Sparse Coding for Machine Learning , Sheng Yang Lundquist (Dissertation)

Workflow Critical Path: a Data-Oriented Path Metric for Holistic HPC Workflows , Daniel D. Nguyen (Thesis)

Novel View Synthesis in Time and Space , Simon Niklaus (Dissertation)

Balancing Security, Performance and Deployability in Encrypted Search , David Joel Pouliot (Dissertation)

Theses/Dissertations from 2019 2019

A Secure Anti-Counterfeiting System using Near Field Communication, Public Key Cryptography, Blockchain, and Bayesian Games , Naif Saeed Alzahrani (Dissertation)

Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series , Logan Blakely (Thesis)

Local Radiance , Scott Peter Britell (Dissertation)

Correct-by-Construction Typechecking with Scope Graphs , Katherine Imhoff Casamento (Thesis)

Versatile Binary-level Concolic Testing , Bo Chen (Dissertation)

Crumpled and Abraded Encryption: Implementation and Provably Secure Construction , Scott Sherlock Griffy (Thesis)

Knowing Without Knowing: Real-Time Usage Identification of Computer Systems , Leila Mohammed Hawana (Thesis)

Design and Experimental Evaluation of DeepMarket: an Edge Computing Marketplace with Distributed TensorFlow Execution Capability , Soyoung Kim (Thesis)

Localizing Little Landmarks with Transfer Learning , Sharad Kumar (Thesis)

Context-Aware Wi-Fi Infrastructure-based Indoor Positioning Systems , Huy Phuong Tran (Dissertation)

Theses/Dissertations from 2018 2018

Bounding Box Improvement with Reinforcement Learning , Andrew Lewis Cleland (Thesis)

Sensing Building Structure Using UWB Radios for Disaster Recovery , Jeong Eun Lee (Dissertation)

Annotation-Enabled Interpretation and Analysis of Time-Series Data , Niveditha Venugopal (Thesis)

EPA-RIMM-V: Efficient Rootkit Detection for Virtualized Environments , Tejaswini Ajay Vibhute (Thesis)

Theses/Dissertations from 2017 2017

Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs , Erik Timothy Conser (Thesis)

Refining Bounding-Box Regression for Object Localization , Naomi Lynn Dickerson (Thesis)

Fully Generic Programming Over Closed Universes of Inductive-Recursive Types , Larry Diehl (Dissertation)

Communicating at Terahertz Frequencies , Farnoosh Moshirfatemi (Dissertation)

Designing In-Headset Authoring Tools for Virtual Reality Video , Cuong Nguyen (Dissertation)

Certifying Loop Pipelining Transformations in Behavioral Synthesis , Disha Puri (Dissertation)

Power-Aware Datacenter Networking and Optimization , Qing Yi (Dissertation)

Theses/Dissertations from 2016 2016

Identifying Relationships between Scientific Datasets , Abdussalam Alawini (Dissertation)

Information Representation and Computation of Spike Trains in Reservoir Computing Systems with Spiking Neurons and Analog Neurons , Amin Almassian (Thesis)

Investigations of an "Objectness" Measure for Object Localization , Lewis Richard James Coates (Thesis)

Image Stitching: Handling Parallax, Stereopsis, and Video , Fan Zhang (Dissertation)

Theses/Dissertations from 2015 2015

Novel Methods for Learning and Adaptation in Chemical Reaction Networks , Peter Banda (Dissertation)

Post-silicon Functional Validation with Virtual Prototypes , Kai Cong (Dissertation)

Novel Cryptographic Primitives and Protocols for Censorship Resistance , Kevin Patrick Dyer (Dissertation)

Hardware/Software Interface Assurance with Conformance Checking , Li Lei (Dissertation)

Leveraging Contextual Relationships Between Objects for Localization , Clinton Leif Olson (Thesis)

The Performance of Random Prototypes in Hierarchical Models of Vision , Kendall Lee Stewart (Thesis)

Tweakable Ciphers: Constructions and Applications , Robert Seth Terashima (Dissertation)

Scalable Equivalence Checking for Behavioral Synthesis , Zhenkun Yang (Dissertation)

Theses/Dissertations from 2014 2014

The Nax Language: Unifying Functional Programming and Logical Reasoning in a Language based on Mendler-style Recursion Schemes and Term-indexed Types , Ki Yung Ahn (Dissertation)

Using Spammers' Computing Resources for Volunteer Computing , Thai Le Quy Bui (Thesis)

Towards Constructing Interactive Virtual Worlds , Francis Chang (Dissertation)

System-wide Performance Analysis for Virtualization , Deron Eugene Jensen (Thesis)

Advances in Piecewise Smooth Image Reconstruction , Ralf Juengling (Dissertation)

Interpretable Machine Learning and Sparse Coding for Computer Vision , Will Landecker (Dissertation)

Optimizing Data Movement in Hybrid Analytic Systems , Patrick Michael Leyshock (Dissertation)

Ranked Similarity Search of Scientific Datasets: An Information Retrieval Approach , Veronika Margaret Megler (Dissertation)

Using GIST Features to Constrain Search in Object Detection , Joanna Browne Solmon (Thesis)

The Role of Prototype Learning in Hierarchical Models of Vision , Michael David Thomure (Dissertation)

Theses/Dissertations from 2013 2013

Object Detection and Recognition in Natural Settings , George William Dittmar (Thesis)

Trust-but-Verify: Guaranteeing the Integrity of User-generated Content in Online Applications , Akshay Dua (Dissertation)

Equivalence Checking for High-Assurance Behavioral Synthesis , Kecheng Hao (Dissertation)

Type Classes and Instance Chains: A Relational Approach , John Garrett Morris (Dissertation)

Theses/Dissertations from 2012 2012

Using Dataflow Optimization Techniques with a Monadic Intermediate Language , Justin George Bailey (Thesis)

A Survey and Analysis of Solutions to the Oblivious Memory Access Problem , Erin Elizabeth Chapman (Thesis)

A Data-Descriptive Feedback Framework for Data Stream Management Systems , Rafael J. Fernández Moctezuma (Dissertation)

Extending Relativistic Programming to Multiple Writers , Philip William Howard (Dissertation)

The Basic Scheme for the Evaluation of Functional Logic Programs , Arthur Peters (Thesis)

The Link Between Image Segmentation and Image Recognition , Karan Sharma (Thesis)

Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures , Josh Triplett (Dissertation)

Theses/Dissertations from 2011 2011

Conceptual Modeling of Data with Provenance , David William Archer (Dissertation)

Low-latency Estimates for Window-Aggregate Queries over Data Streams , Amit Bhat (Thesis)

Information Processing in Two-Dimensional Cellular Automata , Martin Cenek (Dissertation)

Scalable and Efficient Tasking for Dynamic Sensor Networks , Thanh Xuan Dang (Dissertation)

On the Effect of Topology on Learning and Generalization in Random Automata Networks , Alireza Goudarzi (Thesis)

HOLCF '11: A Definitional Domain Theory for Verifying Functional Programs , Brian Charles Huffman (Dissertation)

A Functional Approach to Memory-Safe Operating Systems , Rebekah Leslie (Dissertation)

Factoring Semiprimes Using PG2N Prime Graph Multiagent Search , Keith Eirik Wilson (Thesis)

High Speed Wireless Networking for 60GHz , Candy Yiu (Dissertation)

Theses/Dissertations from 2010 2010

Extensible Scheduling in a Haskell-based Operating System , Kenneth William Graunke (Thesis)

Addressing Automated Adversaries of Network Applications , Edward Leo Kaiser (Dissertation)

An Automata-Theoretic Approach to Hardware/Software Co-verification , Juncao Li (Dissertation)

Practical Type Inference for the GADT Type System , Chuan-kai Lin (Dissertation)

Scalable event tracking on high-end parallel systems , Kathryn Marie Mohror (Dissertation)

Performance Analysis of Hybrid CPU/GPU Environments , Michael Shawn Smith (Thesis)

Theses/Dissertations from 2009 2009

Computational Techniques for Reducing Spectra of the Giant Planets in Our Solar System , Holly L. Grimes (Thesis)

Programmer Friendly Refactoring Tools , Emerson Murphy-Hill (Dissertation)

A Framework for Superimposed Applications : Techniques to Represent, Access, Transform, and Interchange Bi-level Information , Sudarshan Srivivasa Murthy (Dissertation)

Graphical User Interfaces as Updatable Views , James Felger Terwilliger (Dissertation)

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All of our academic staff are research active, working with a team of post-graduate and post-doctoral researchers and a lively population of research students. Our research focuses on core themes of theoretical and practical computer science: artificial intelligence and symbolic computation, networked and distributed systems, systems engineering, and human computer interaction.

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Home > College of Natural Sciences > COMPUTERSCI-ENGINEERING > COMPUTERSCI-ENGINEERING-ETD

Computer Science and Engineering Theses, Projects, and Dissertations

Theses/projects/dissertations from 2024 2024.

TRAFFIC ANALYSIS OF CITIES IN SAN BERNARDINO COUNTY , Sai Kalyan Ayyagari

Recommendation System using machine learning for fertilizer prediction , Durga Rajesh Bommireddy

Classification of Remote Sensing Image Data Using Rsscn-7 Dataset , Satya Priya Challa

Cultural Awareness Application , Bharat Gupta

PREDICTING HOSPITALIZATION USING ARTIFICIAL INTELLIGENCE , Sanath Hiremath

AUTOMATED BRAIN TUMOR CLASSIFIER WITH DEEP LEARNING , venkata sai krishna chaitanya kandula

TRUCK TRAFFIC ANALYSIS IN THE INLAND EMPIRE , Bhavik Khatri

Crash Detecting System Using Deep Learning , Yogesh Reddy Muddam

A SMART HYBRID ENHANCED RECOMMENDATION AND PERSONALIZATION ALGORITHM USING MACHINE LEARNING , Aswin Kumar Nalluri

Theses/Projects/Dissertations from 2023 2023

CLASSIFICATION OF LARGE SCALE FISH DATASET BY DEEP NEURAL NETWORKS , Priyanka Adapa

GEOSPATIAL WILDFIRE RISK PREDICTION USING DEEP LEARNING , Abner Alberto Benavides

HUMAN SUSPICIOUS ACTIVITY DETECTION , Nilamben Bhuva

MAX FIT EVENT MANAGEMENT WITH SALESFORCE , AKSHAY DAGWAR

MELANOMA DETECTION BASED ON DEEP LEARNING NETWORKS , Sanjay Devaraneni

Heart Disease Prediction Using Binary Classification , Virendra Sunil Devare

CLASSIFICATION OF THORAX DISEASES FROM CHEST X-RAY IMAGES , Sharad Jayusukhbhai Dobariya

WEB BASED MANAGEMENT SYSTEM FOR HOUSING SOCIETY , Likhitha Reddy Eddala

Sales and Stock Management System , Rashmika Gaddam Ms

CONTACTLESS FOOD ORDERING SYSTEM , Rishivar Kumar Goli

RESTAURANT MANAGEMENT WEBSITE , Akhil Sai Gollapudi

DISEASE OF LUNG INFECTION DETECTION USING CNN MODEL -BAYESIAN OPTIMIZATION , poojitha gutha

DATA POISONING ATTACKS ON PHASOR MEASUREMENT UNIT DATA , Rutuja Sanjeev Haridas

CRIME MAPPING ANALYSIS USING WEB APPLICATION. , Lavanya Krishnappa

A LONG-TERM FUNDS PREDICTOR BASED ON DEEP LEARNING , SHUIYI KUANG

LIVER SEGMENTATION AND LESION DETECTION IN MEDICAL IMAGES USING A DEEP LEARNING-BASED U-NET MODEL , Kaushik Mahida

PHASOR MEASUREMENT UNIT DATA VISUALIZATION , Nikhila Mandava

TWITTER POLICING , Hemanth Kumar Medisetty

TRANSACTION MANAGEMENT SYSYEM FOR A PUBLISHER , HASSAIN SHAREEF MOHAMMED JR

LOBANGU: AN OPTICAL CHARACTER RECOGNITION RECEIPT MANAGEMENT APP FOR HEALTH CENTER PHARMACIES IN THE D.R.CONGO AND SURROUNDING EASTERN AFRICAN COUNTRIES , Bénis Munganga

PREDICTIVE MODEL FOR CFPB CONSUMER COMPLAINTS , Vyshnavi Nalluri

REVIEW CLASSIFICATION USING NATURAL LANGUAGE PROCESSING AND DEEP LEARNING , Brian Nazareth

Brain Tumor Detection Using MRI Images , Mayur Patel

QUIZ WEB APPLICATION , Dipti Rathod

HYPOTHYROID DISEASE ANALYSIS BY USING MACHINE LEARNING , SANJANA SEELAM

Pillow Based Sleep Tracking Device Using Raspberry Pi , Venkatachalam Seviappan

FINSERV ANDROID APPLICATION , Harsh Piyushkumar Shah

AUTOMATED MEDICAL NOTES LABELLING AND CLASSIFICATION USING MACHINE LEARNING , Akhil Prabhakar Thota

GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET , Asitha Thumpati

GOVERNMENT AID PORTAL , Darshan Togadiya

GENERAL POPULATION PROJECTION MODEL WITH CENSUS POPULATION DATA , Takenori Tsuruga

LUNG LESION SEGMENTATION USING DEEP LEARNING APPROACHES , Sree Snigdha Tummala

DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING , Saranya Valleri

Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation , Peter Wright

Theses/Projects/Dissertations from 2022 2022

LEARN PROGRAMMING IN VIRTUAL REALITY? A PROJECT FOR COMPUTER SCIENCE STUDENTS , Benjamin Alexander

LUNG CANCER TYPE CLASSIFICATION , Mohit Ramajibhai Ankoliya

HIGH-RISK PREDICTION FOR COVID-19 PATIENTS USING MACHINE LEARNING , Raja Kajuluri

IMPROVING INDIA’S TRAFFIC MANAGEMENT USING INTELLIGENT TRANSPORTATION SYSTEMS , Umesh Makhloga

DETECTION OF EPILEPSY USING MACHINE LEARNING , Balamurugan Murugesan

SOCIAL MOBILE APPLICATION: UDROP , Mahmoud Oraiqat

Improved Sensor-Based Human Activity Recognition Via Hybrid Convolutional and Recurrent Neural Networks , Sonia Perez-Gamboa

College of Education FileMaker Extraction and End-User Database Development , Andrew Tran

DEEP LEARNING EDGE DETECTION IN IMAGE INPAINTING , Zheng Zheng

Theses/Projects/Dissertations from 2021 2021

A General Conversational Chatbot , Vipin Nambiar

Verification System , Paras Nigam

DESKTOP APPLICATION FOR THE PUZZLE BOARD GAME “RUSH HOUR” , Huanqing Nong

Ahmedabad City App , Rushabh Picha

COMPUTER SURVEILLANCE SYSTEM USING WI-FI FOR ANDROID , Shashank Reddy Saireddy

ANDROID PARKING SYSTEM , Vishesh Reddy Sripati

Sentiment Analysis: Stock Index Prediction with Multi-task Learning and Word Polarity Over Time , Yue Zhou

Theses/Projects/Dissertations from 2020 2020

BUBBLE-IN DIGITAL TESTING SYSTEM , Chaz Hampton

FEEDBACK REVIEW SYSTEM USING SENTIMENT ANALYSIS , Vineeth Kukkamalla

WEB APPLICATION FOR MOVIE PERFORMANCE PREDICTION , Devalkumar Patel

Theses/Projects/Dissertations from 2019 2019

REVIEWS TO RATING CONVERSION AND ANALYSIS USING MACHINE LEARNING TECHNIQUES , Charitha Chanamolu

EASY EXAM , SARTHAK DABHI

EXTRACT TRANSFORM AND LOADING TOOL FOR EMAIL , Amit Rajiv Lawanghare

VEHICLE INFORMATION SYSTEM USING BLOCKCHAIN , Amey Zulkanthiwar

Theses/Projects/Dissertations from 2018 2018

USING AUTOENCODER TO REDUCE THE LENGTH OF THE AUTISM DIAGNOSTIC OBSERVATION SCHEDULE (ADOS) , Sara Hussain Daghustani

California State University, San Bernardino Chatbot , Krutarth Desai

ORGANIZE EVENTS MOBILE APPLICATION , Thakshak Mani Chandra Reddy Gudimetla

SOCIAL NETWORK FOR SOFTWARE DEVELOPERS , Sanket Prabhakar Jadhav

VIRTUALIZED CLOUD PLATFORM MANAGEMENT USING A COMBINED NEURAL NETWORK AND WAVELET TRANSFORM STRATEGY , Chunyu Liu

INTER PROCESS COMMUNICATION BETWEEN TWO SERVERS USING MPICH , Nagabhavana Narla

SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES , Arumugam Thendramil Pavai

NEURAL NETWORK ON VIRTUALIZATION SYSTEM, AS A WAY TO MANAGE FAILURE EVENTS OCCURRENCE ON CLOUD COMPUTING , Khoi Minh Pham

EPICCONFIGURATOR COMPUTER CONFIGURATOR AND CMS PLATFORM , IVO A. TANTAMANGO

STUDY ON THE PATTERN RECOGNITION ENHANCEMENT FOR MATRIX FACTORIZATIONS WITH AUTOMATIC RELEVANCE DETERMINATION , hau tao

Theses/Projects/Dissertations from 2017 2017

CHILDREN’S SOCIAL NETWORK: KIDS CLUB , Eiman Alrashoud

MULTI-WAY COMMUNICATION SYSTEM , S. Chinnam

WEB APPLICATION FOR GRADUATE COURSE RECOMMENDATION SYSTEM , Sayali Dhumal

MOBILE APPLICATION FOR ATTENDANCE SYSTEM COYOTE-ATTENDANCE , Sindhu Hari

WEB APPLICATION FOR GRADUATE COURSE ADVISING SYSTEM , Sanjay Karrolla

Custom T-Shirt Designs , Ranjan Khadka

STUDENT CLASS WAITING LIST ENROLLMENT , AISHWARYA LACHAGARI

ANDROID MOBILE APPLICATION FOR HOSPITAL EXECUTIVES , Vihitha Nalagatla

PIPPIN MACHINE , Kiran Reddy Pamulaparthy

SOUND MODE APPLICATION , Sindhuja Pogaku

I2MAPREDUCE: DATA MINING FOR BIG DATA , Vishnu Vardhan Reddy Sherikar

COMPARING AND IMPROVING FACIAL RECOGNITION METHOD , Brandon Luis Sierra

NATURAL LANGUAGE PROCESSING BASED GENERATOR OF TESTING INSTRUMENTS , Qianqian Wang

AUTOMATIC GENERATION OF WEB APPLICATIONS AND MANAGEMENT SYSTEM , Yu Zhou

Theses/Projects/Dissertations from 2016 2016

CLOTH - MODELING, DEFORMATION, AND SIMULATION , Thanh Ho

CoyoteLab - Linux Containers for Educational Use , Michael D. Korcha

PACKET FILTER APPROACH TO DETECT DENIAL OF SERVICE ATTACKS , Essa Yahya M Muharish

DATA MINING: TRACKING SUSPICIOUS LOGGING ACTIVITY USING HADOOP , Bir Apaar Singh Sodhi

Theses/Projects/Dissertations from 2015 2015

APPLY DATA CLUSTERING TO GENE EXPRESSION DATA , Abdullah Jameel Abualhamayl Mr.

Density Based Data Clustering , Rayan Albarakati

Developing Java Programs on Android Mobile Phones Using Speech Recognition , Santhrushna Gande

THE DESIGN AND IMPLEMENTATION OF AN ADAPTIVE CHESS GAME , Mehdi Peiravi

CALIFORNIA STATE UNIVERSITY SAN BERNARDINO WiN GPS , Francisco A. Ron

ESTIMATION ON GIBBS ENTROPY FOR AN ENSEMBLE , Lekhya Sai Sake

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Computer Networking Dissertation Topics

Published by Carmen Troy at January 5th, 2023 , Revised On May 16, 2024

A dissertation is an essential aspect of completing your degree program. Whether you are pursuing your master’s or are enrolled in a PhD program, you will not be awarded a degree without successfully submitting a thesis. To ensure that your thesis is submitted successfully without any hindrances, you should first get your topic and dissertation outline approved by your professor. When approving, supervisors focus on a lot of aspects.

However, relevance, recency, and conciseness play a huge role in accepting or rejecting your topic.

As a computer networking student, you have a variety of networking topics to choose from. With the field evolving with each passing day, you must ensure that your thesis covers recent computer networking topics and explores a relevant problem or issue. To help you choose the right topic for your dissertation, here is a list of recent and relevant computer networking dissertation topics.

List Of Trending Ideas For Your Computer Networking Dissertation

Machine learning for proactive network anomaly detection
The role of software-defined-networking (SDN) for network performance and security
Applications and challenges of 6G technologies
How to ensure fairness and efficiency in Multi-Access Edge Computing (MEC)
Denial-of-Service (DoS) Attacks in the Age of Distributed Denial-of-Service (DDoS) Attacks
Applications and rise of Low-Power Wide Area Networks (LPWANs)
Efficient Resource Allocation and Quality-of-Service (QoS) Management
Ethical Implications of Artificial Intelligence (AI) in Network Management
The best ways to use Blockchain for Tamper-Proof Evidence Collection and Storage
Role of Network Operators in Cloud Gaming

Computer Networking Dissertation Topics For Your Research

Topic 1: an evaluation of the network security during machine to machine communication in iot.

Research Aim: The research aims to evaluate the network security issues associated with M2M communication in IoT.

Objectives:

To evaluate the factors affecting the network security of IoT devices.
To determine the methods for increasing data integrity in M2M communication against physical tampering and unauthorised monitoring.
To evaluate the network security issues associated with M2M communication in IoT and offer suitable recommendations for improvement.

Topic 2: An analysis of the cybersecurity challenges in public clouds and appropriate intrusion detection mechanisms.

Research Aim: The aim of the research is to analyse the cybersecurity challenges in public clouds and the appropriate intrusion detection mechanisms.

Objectives:

To analyse the types of cybersecurity threats impacting public clouds.
To determine some of the competent intrusion detection techniques that can be used in cloud computing.
To investigate the cybersecurity challenges in public clouds and offer mitigating with appropriate intrusion detection techniques.

Topic 3: Investigating the impact of SaaS cloud ERP on the scalability and cost-effectiveness of business.

Research Aim: The research aims to investigate the impact of SaaS cloud ERP on the scalability and cost-effectiveness of business.

To analyse the benefits of SaaS ERP over traditional ERP.
To evaluate the characteristics of SaaS architecture in cloud computing and determine its varieties.
To investigate how SaaS cloud ERP impacts business scalability and cost-effectiveness.

Topic 4: An evaluation of the requirements of cloud repatriation and the challenges associated with it.

Research Aim: The research aims to evaluate the requirements of cloud repatriation in organisations and the associated challenges

To analyse the key factors of cloud repatriation.
To determine the challenges associated with cloud repatriation from public clouds.
To evaluate the need for cloud repatriation in organisations and the associated complexities

Topic 5: An examination of the security mechanisms in decentralised networks and the ways of enhancing system robustness

Research Aim: The research aims to investigate the security mechanisms in decentralised networks and the ways of enhancing system robustness.

To analyse the concept of decentralised networks and understand their difference from centralised networks.
To analyse the security mechanisms in decentralised networks to determine how it offers visibility and traceability.
To investigate the security mechanisms in decentralised networks and how system robustness can be increased for better privacy and security.

Latest Computer Networking Dissertation Topics

Exploring the importance of computer networking in today’s era.

Research Aim: Even though computer networking has been practised for a few years now, its importance has increased immensely over the past two years. A few main reasons include the use of technology by almost every business and the aim to offer customers an easy and convenient shopping experience. The main aim of this research will be to explain the concepts of computer networking, its benefits, and its importance in the current era. The research will also discuss how computer networking has helped businesses and individuals perform their work and benefit from it. The research will then specifically state examples where computer networking has brought positive changes and helped people achieve what they want.

Wireless Networks in Business Settings – An Analysis

Research Aim: Wireless networks are crucial in computer networking. They help build networks seamlessly, and once the networks are set up on a wireless network, it becomes extremely easy for the business to perform its daily activities. This research will investigate all about wireless networks in a business setting. It will first introduce the various wireless networks that can be utilised by a business and will then talk about how these networks help companies build their workflow around them. The study will analyse different wireless networks used by businesses and will conclude how beneficial they are and how they are helping the business.

Understanding Virtual Private Networks – A Deep Analysis of Their Challenges

Research Aim: Private virtual networks (VPN) are extremely common today. These are used by businesses and individuals alike. This research aims to understand how these networks operate and how they help businesses build strong and successful systems and address the challenges of VPNs. A lot of businesses do not adopt virtual private networks due to the challenges that they bring. This research will address these challenges in a way that will help businesses implement VPNs successfully.

A Survey of the Application of Wireless Sensor Networks

Research Aim: Wireless sensor networks are self-configured, infrastructure-less wireless networks to pass data. These networks are now extremely popular amongst businesses because they can solve problems in various application domains and possess the capacity to change the way work is done. This research will investigate where wireless sensor networks are implemented, how they are being used, and how they are performing. The research will also investigate how businesses implement these systems and consider factors when utilising these wireless sensor networks.

Computer Network Security Attacks – Systems and Methods to Respond

Research Aim: With the advent of technology today, computer networks are extremely prone to security attacks. A lot of networks have security systems in place. However, people with nefarious intent find one way to intrude and steal data/information. This research will address major security attacks that have impacted businesses and will aim to address this challenge. Various methods and systems will be highlighted to protect the computer networks. In addition to this, the research will also discuss various methods to respond to attacks and to keep the business network protected.

Preventing a Cyberattack – How Can You Build a Powerful Computer Network?

Research Aim: Cyberattacks are extremely common these days. No matter how powerful your network is, you might be a victim of phishing or hacking. The main aim of this research will be to outline how a powerful computer network can be built. Various methods to build a safe computer network that can keep data and information will be outlined, and the study will also highlight ways to prevent a cyberattack. In addition to this, the research will talk about the steps that should be taken to keep the computer network safe. The research will conclude with the best way and system to build a powerful and safe computer network.

Types of Computer Networks: A Comparison and Analysis

Research Aim: There are different types of computer networks, including LAN, WAN, PAN, MAN, CAN, SAN, etc. This research will discuss all the various types of computer networks to help readers understand how all these networks work. The study will then compare the different types of networks and analyse how each of them is implemented in different settings. The dissertation will also discuss the type of computer networks that businesses should use and how they can use them for their success. The study will then conclude which computer network is the best and how it can benefit when implemented.

Detecting Computer Network Attacks by Signatures and Fast Content Analysis

Research Aim: With technological advancement, today, many computer network attacks can be detected beforehand. While many techniques are utilised for detecting these attacks, the use of signatures and fast content analysis are the most popular ones. This research will explore these techniques in detail and help understand how they can detect a computer network attack and prevent it. The research will present different ways these techniques are utilised to detect an attack and help build powerful and safe computer networks. The research will then conclude how helpful these two techniques are and whether businesses should implement them.

Overview of Wireless Network Technologies and their Role in Healthcare

Research Aim: Wireless network technologies are utilised by several industries. Their uses and benefits have helped businesses resolve many business problems and assisted them in conducting their daily activities without any hindrance. This networking topic will help explore how wireless network technologies work and will talk about their benefits. This research aims to find out how wireless technologies help businesses carry out their daily routine tasks effortlessly. For this research, the focus will be on the healthcare industry. The study will investigate how wireless network technology has helped the healthcare sector and how it has benefited them to perform their daily tasks without much effort.

Setting up a Business Communication System over a Computer Network

Research Aim: Communication is an essential aspect of every business. Employees need to communicate effectively to keep the business going. In the absence of effective communication, businesses suffer a lot as the departments are not synchronised, and the operations are haphazard. This research will explore the different ways through which network technologies help conduct smooth and effective communication within organisations. This research will conclude how wireless networks have helped businesses build effective communication systems within their organisation and how they have benefited from it. It will then conclude how businesses have improved and solved major business problems with the help of these systems.

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How to find computer networking dissertation topics.

To find computer networking dissertation topics:

Follow industry news and emerging technologies.
Investigate unresolved networking challenges.
Review recent research papers.
Explore IoT, cybersecurity , and cloud computing.
Consider real-world applications.
Select a topic aligned with your expertise and career aspirations.

The M.S. Thesis Track

The MS Thesis track is for students who want to concentrate on research in some sub-field of Computer Science. You are required to arrange for a Computer Science Faculty member who agrees to advise the thesis and the rest of your course selection prior to selecting the track.

SUMMARY OF REQUIREMENTS

Complete a total of 30 points (Courses must be at the 4000 level or above)
Maintain at least a 2.7 overall GPA. (No more than 1 D is permitted).
Complete the Columbia Engineering Professional Development & Leadership (PDL) requirement
Satisfy breadth requirements
Take at least 6 points of technical courses at the 6000 level
At most, up to 3 points of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Submit the Non-CS/NonTrack form and the course syllabus to your CS Faculty Advisor for review

1. BREADTH REQUIREMENT

Visit the breadth requirement page for more information.

2. REQUIRED TRACK COURSES (9 credits)

Students must take 9 credits of COMS E6902 Thesis. The points are typically spread over multiple semesters, e.g., 3 points each for 3 semesters or 4.5 points each for 2 semesters. No more than 9 points of E6902 may be taken. Sign up for the section number of E6902 associated with your thesis advisor.

3. ELECTIVE TRACK COURSES

Students are required to complete 9 elective credits of graduate courses (4000-level or above) selected from Computer Science and/or related areas together with your faculty thesis advisor. These would normally be strongly related to your thesis topic.

Up to 3 of these points may be in COMS E6901 Projects in Computer Science.

Please note:

The degree progress checklist should be used to keep track of your requirements. if you have questions for your track advisor or cs advising, you should have an updated checklist prepared, due to a significant overlap in course material, ms students not in the machine learning track can only take 1 of the following courses – coms 4771, coms 4721, elen 4903, ieor 4525, stat 4240, stat 4400/4241/5241 – as part of their degree requirements, the elective track courses cannot be imported from another institution., 4. general electives.

Students must complete the remaining credits of General Elective Courses at the 4000 level or above. At least three of these points must be chosen from either the Track Electives listed above or from the CS department at the 4000 level or higher.

Students may also request to use at most 3 points of Non-CS/Non-Track coursework if approved by the process listed below.

5. THESIS DEFENSE

A thesis proposal is presented to your thesis committee at least three months before your defense. Your thesis committee should have three members. Two of them must be internal, but one can be an outsider. Please bring the thesis defense form to your defense. Once completed, please submit the form to CS Advising via email: [email protected].

The thesis cannot be imported from another institution.

A publication-quality thesis document is also published as a CS department technical report. Once completed, please upload your thesis into MICE.

PROGRAM PLANNING

Please visit the Directory of Classes to get the updated course listings. Please also note that not all courses are offered every semester or even every year. A few courses are offered only once every two or three years or even less frequently.

Updated: 3/26/2024

Find open faculty positions here .

Computer Science at Columbia University

Upcoming events, in the news, press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Home > FACULTIES > Computer Science > CSD-ETD

Computer Science Theses and Dissertations

This collection contains theses and dissertations from the Department of Computer Science, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository

Theses/Dissertations from 2024 2024

A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR , Farhad Dalirani

Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications , Sudipta Singha Roy

Theses/Dissertations from 2023 2023

Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis , Amreen Anbar

Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces , Sepehr Asgarian

Improved Protein Sequence Alignments Using Deep Learning , Seyed Sepehr Ashrafzadeh

INVESTIGATING IMPROVEMENTS TO MESH INDEXING , Anurag Bhattacharjee

Algorithms and Software for Oligonucleotide Design , Qin Dong

Framework for Assessing Information System Security Posture Risks , Syed Waqas Hamdani

De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling , Fang Han

Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions , Heather Hartley

A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit , Rongji He

A Novel Method for Assessment of Batch Effect on single cell RNA sequencing data , Behnam Jabbarizadeh

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs , Taabish Jeshani

Citation Polarity Identification From Scientific Articles Using Deep Learning Methods , Souvik Kundu

Denoising-Based Domain Adaptation Network for EEG Source Imaging , Runze Li

Decoy-Target Database Strategy and False Discovery Rate Analysis for Glycan Identification , Xiaoou Li

DpNovo: A DEEP LEARNING MODEL COMBINED WITH DYNAMIC PROGRAMMING FOR DE NOVO PEPTIDE SEQUENCING , Yizhou Li

Developing A Smart Home Surveillance System Using Autonomous Drones , Chongju Mai

Look-Ahead Selective Plasticity for Continual Learning , Rouzbeh Meshkinnejad

The Two Visual Processing Streams Through The Lens Of Deep Neural Networks , Aidasadat Mirebrahimi Tafreshi

Source-free Domain Adaptation for Sleep Stage Classification , Yasmin Niknam

Data Heterogeneity and Its Implications for Fairness , Ghazaleh Noroozi

Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments , Elham Okhovat

Evaluating the Likelihood of Bug Inducing Commits Using Metrics Trend Analysis , Parul Parul

On Computing Optimal Repairs for Conditional Independence , Alireza Pirhadi

Open-Set Source-Free Domain Adaptation in Fundus Images Analysis , Masoud Pourreza

Migration in Edge Computing , Arshin Rezazadeh

A Modified Hopfield Network for the K-Median Problem , Cody Rossiter

Predicting Network Failures with AI Techniques , Chandrika Saha

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective , Sajal Saha

An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support , Mozhgan Salimiparsa

Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition , Amir Hesam Salimnia

Global Cyber Attack Forecast using AI Techniques , Nusrat Kabir Samia

IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION , Rafael David Segistan Canizales

A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods , Kayvan Shabani

Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network , Sareh Soltani Nejad

Smartphone Loss Prevention System Using BLE and GPS Technology , Noshin Tasnim

A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets) , Yasmen M. Wahba

Reducing Negative Transfer of Random Data in Source-Free Unsupervised Domain Adaptation , Anthony Wong

Deep Neural Methods for True/Pseudo- Invasion Classification in Colorectal Polyp Whole-Slide Images , Zhiyuan Yang

Developing a Relay-based Autonomous Drone Delivery System , Muhammad Zakar

Learning Mortality Risk for COVID-19 Using Machine Learning and Statistical Methods , Shaoshi Zhang

Machine Learning Techniques for Improved Functional Brain Parcellation , Da Zhi

Theses/Dissertations from 2022 2022

The Design and Implementation of a High-Performance Polynomial System Solver , Alexander Brandt

Defining Service Level Agreements in Serverless Computing , Mohamed Elsakhawy

Algorithms for Regular Chains of Dimension One , Juan P. Gonzalez Trochez

Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications , Susmitha Hanumanthu

Multi-Device Data Analysis for Fault Localization in Electrical Distribution Grids , Jacob D L Hunte

Towards Parking Lot Occupancy Assessment Using Aerial Imagery and Computer Vision , John Jewell

Potential of Vision Transformers for Advanced Driver-Assistance Systems: An Evaluative Approach , Andrew Katoch

Psychological Understanding of Textual journals using Natural Language Processing approaches , Amirmohammad Kazemeinizadeh

Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems , Nima Khairdoost

Solving Challenges in Deep Unsupervised Methods for Anomaly Detection , Vahid Reza Khazaie

Developing an Efficient Real-Time Terrestrial Infrastructure Inspection System Using Autonomous Drones and Deep Learning , Marlin Manka

Predictive Modelling For Topic Handling Of Natural Language Dialogue With Virtual Agents , Lareina Milambiling

Improving Deep Entity Resolution by Constraints , Soudeh Nilforoushan

Respiratory Pattern Analysis for COVID-19 Digital Screening Using AI Techniques , Annita Tahsin Priyoti

Extracting Microservice Dependencies Using Log Analysis , Andres O. Rodriguez Ishida

False Discovery Rate Analysis for Glycopeptide Identification , Shun Saito

Towards a Generalization of Fulton's Intersection Multiplicity Algorithm , Ryan Sandford

An Investigation Into Time Gazed At Traffic Objects By Drivers , Kolby R. Sarson

Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives , Ibrahim Mohammed Sayem

A Unified Representation and Deep Learning Architecture for Persuasive Essays in English , Muhammad Tawsif Sazid

Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP) , Aakriti Sharma

Advances in the Automatic Detection of Optimization Opportunities in Computer Programs , Delaram Talaashrafi

Reputation-Based Trust Assessment of Transacting Service Components , Konstantinos Tsiounis

Fully Autonomous UAV Exploration in Confined and Connectionless Environments , Kirk P. Vander Ploeg

Three Contributions to the Theory and Practice of Optimizing Compilers , Linxiao Wang

Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach , Wumian Wang

Predicting and Modifying Memorability of Images , Mohammad Younesi

Theses/Dissertations from 2021 2021

Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches , Mahtab Ahmed

A Physical Layer Framework for a Smart City Using Accumulative Bayesian Machine Learning , Razan E. AlFar

Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning , Aseel AlOrbani

Contrastive Learning of Auditory Representations , Haider Al-Tahan

Cache-Friendly, Modular and Parallel Schemes For Computing Subresultant Chains , Mohammadali Asadi

Protein Interaction Sites Prediction using Deep Learning , Sourajit Basak

Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis , William A. Beldman

Improving Reader Motivation with Machine Learning , Tanner A. Bohn

A Black-box Approach for Containerized Microservice Monitoring in Fog Computing , Shi Chang

Visualization and Interpretation of Protein Interactions , Dipanjan Chatterjee

A Framework for Characterising Performance in Multi-Class Classification Problems with Applications in Cancer Single Cell RNA Sequencing , Erik R. Christensen

Exploratory Search with Archetype-based Language Models , Brent D. Davis

Evolutionary Design of Search and Triage Interfaces for Large Document Sets , Jonathan A. Demelo

Building Effective Network Security Frameworks using Deep Transfer Learning Techniques , Harsh Dhillon

A Deep Topical N-gram Model and Topic Discovery on COVID-19 News and Research Manuscripts , Yuan Du

Automatic extraction of requirements-related information from regulatory documents cited in the project contract , Sara Fotouhi

Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones , Gopi Gugan

A Visual Analytics System for Rapid Sensemaking of Scientific Documents , Amirreza Haghverdiloo Barzegar

Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm , Mohammad Karami

Fuzzy and Probabilistic Rule-Based Approaches to Identify Fault Prone Files , Piyush Kumar Korlepara

Parallel Arbitrary-precision Integer Arithmetic , Davood Mohajerani

A Technique for Evaluating the Health Status of a Software Module Using Process Metrics , . Ria

Visual Analytics for Performing Complex Tasks with Electronic Health Records , Neda Rostamzadeh

Predictive Model of Driver's Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems , Mohsen Shirpour

A Generative-Discriminative Approach to Human Brain Mapping , Deepanshu Wadhwa

WesternAccelerator：Rapid Development of Microservices , Haoran Wei

A Lightweight and Explainable Citation Recommendation System , Juncheng Yin

Mitosis Detection from Pathology Images , Jinhang Zhang

Theses/Dissertations from 2020 2020

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury , Sheikh S. Abdullah

Towards the Development of Network Service Cost Modeling-An ISP Perspective , Yasmeen Ali

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DASH , Digital Access to Scholarship at Harvard, is the university's central, open-access repository for the scholarly output of faculty and the broader research community at Harvard. Most Ph.D. dissertations submitted from March 2012 forward are available online in DASH.
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A list of completed theses and new thesis topics from the Computer Vision Group.

Are you about to start a BSc or MSc thesis? Please read our instructions for preparing and delivering your work.

Below we list possible thesis topics for Bachelor and Master students in the areas of Computer Vision, Machine Learning, Deep Learning and Pattern Recognition. The project descriptions leave plenty of room for your own ideas. If you would like to discuss a topic in detail, please contact the supervisor listed below and Prof. Paolo Favaro to schedule a meeting. Note that for MSc students in Computer Science it is required that the official advisor is a professor in CS.

AI deconvolution of light microscopy images

Level: master.

Background Light microscopy became an indispensable tool in life sciences research. Deconvolution is an important image processing step in improving the quality of microscopy images for removing out-of-focus light, higher resolution, and beter signal to noise ratio. Currently classical deconvolution methods, such as regularisation or blind deconvolution, are implemented in numerous commercial software packages and widely used in research. Recently AI deconvolution algorithms have been introduced and being currently actively developed, as they showed a high application potential.

Aim Adaptation of available AI algorithms for deconvolution of microscopy images. Validation of these methods against state-of-the -art commercially available deconvolution software.

Material and Methods Student will implement and further develop available AI deconvolution methods and acquire test microscopy images of different modalities. Performance of developed AI algorithms will be validated against available commercial deconvolution software.

Al algorithm development and implementation: 50%.
Data acquisition: 10%.
Comparison of performance: 40 %.

Requirements

Interest in imaging.
Solid knowledge of AI.
Good programming skills.

Supervisors Paolo Favaro, Guillaume Witz, Yury Belyaev.

Institutes Computer Vison Group, Digital Science Lab, Microscopy imaging Center.

Contact Yury Belyaev, Microscopy imaging Center, [email protected] , + 41 78 899 0110.

Instance segmentation of cryo-ET images

Level: bachelor/master.

In the 1600s, a pioneering Dutch scientist named Antonie van Leeuwenhoek embarked on a remarkable journey that would forever transform our understanding of the natural world. Armed with a simple yet ingenious invention, the light microscope, he delved into uncharted territory, peering through its lens to reveal the hidden wonders of microscopic structures. Fast forward to today, where cryo-electron tomography (cryo-ET) has emerged as a groundbreaking technique, allowing researchers to study proteins within their natural cellular environments. Proteins, functioning as vital nano-machines, play crucial roles in life and understanding their localization and interactions is key to both basic research and disease comprehension. However, cryo-ET images pose challenges due to inherent noise and a scarcity of annotated data for training deep learning models.

Credit: S. Albert et al./PNAS (CC BY 4.0)

To address these challenges, this project aims to develop a self-supervised pipeline utilizing diffusion models for instance segmentation in cryo-ET images. By leveraging the power of diffusion models, which iteratively diffuse information to capture underlying patterns, the pipeline aims to refine and accurately segment cryo-ET images. Self-supervised learning, which relies on unlabeled data, reduces the dependence on extensive manual annotations. Successful implementation of this pipeline could revolutionize the field of structural biology, facilitating the analysis of protein distribution and organization within cellular contexts. Moreover, it has the potential to alleviate the limitations posed by limited annotated data, enabling more efficient extraction of valuable information from cryo-ET images and advancing biomedical applications by enhancing our understanding of protein behavior.

Methods The segmentation pipeline for cryo-electron tomography (cryo-ET) images consists of two stages: training a diffusion model for image generation and training an instance segmentation U-Net using synthetic and real segmentation masks.

1. Diffusion Model Training: a. Data Collection: Collect and curate cryo-ET image datasets from the EMPIAR database (https://www.ebi.ac.uk/empiar/). b. Architecture Design: Select an appropriate architecture for the diffusion model. c. Model Evaluation: Cryo-ET experts will help assess image quality and fidelity through visual inspection and quantitative measures 2. Building the Segmentation dataset: a. Synthetic and real mask generation: Use the trained diffusion model to generate synthetic cryo-ET images. The diffusion process will be seeded from either a real or a synthetic segmentation mask. This will yield to pairs of cryo-ET images and segmentation masks. 3. Instance Segmentation U-Net Training: a. Architecture Design: Choose an appropriate instance segmentation U-Net architecture. b. Model Evaluation: Evaluate the trained U-Net using precision, recall, and F1 score metrics.

By combining the diffusion model for cryo-ET image generation and the instance segmentation U-Net, this pipeline provides an efficient and accurate approach to segment structures in cryo-ET images, facilitating further analysis and interpretation.

References 1. Kwon, Diana. "The secret lives of cells-as never seen before." Nature 598.7882 (2021): 558-560. 2. Moebel, Emmanuel, et al. "Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms." Nature methods 18.11 (2021): 1386-1394. 3. Rice, Gavin, et al. "TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining." Nature Methods (2023): 1-10.

Contacts Prof. Thomas Lemmin Institute of Biochemistry and Molecular Medicine Bühlstrasse 28, 3012 Bern ( [email protected] )

Prof. Paolo Favaro Institute of Computer Science Neubrückstrasse 10 3012 Bern ( [email protected] )

Adding and removing multiple sclerosis lesions with to imaging with diffusion networks

Background multiple sclerosis lesions are the result of demyelination: they appear as dark spots on t1 weighted mri imaging and as bright spots on flair mri imaging. image analysis for ms patients requires both the accurate detection of new and enhancing lesions, and the assessment of atrophy via local thickness and/or volume changes in the cortex. detection of new and growing lesions is possible using deep learning, but made difficult by the relative lack of training data: meanwhile cortical morphometry can be affected by the presence of lesions, meaning that removing lesions prior to morphometry may be more robust. existing ‘lesion filling’ methods are rather crude, yielding unrealistic-appearing brains where the borders of the removed lesions are clearly visible., aim: denoising diffusion networks are the current gold standard in mri image generation [1]: we aim to leverage this technology to remove and add lesions to existing mri images. this will allow us to create realistic synthetic mri images for training and validating ms lesion segmentation algorithms, and for investigating the sensitivity of morphometry software to the presence of ms lesions at a variety of lesion load levels., materials and methods: a large, annotated, heterogeneous dataset of mri data from ms patients, as well as images of healthy controls without white matter lesions, will be available for developing the method. the student will work in a research group with a long track record in applying deep learning methods to neuroimaging data, as well as experience training denoising diffusion networks..

Nature of the Thesis:

Literature review: 10%

Replication of Blob Loss paper: 10%

Implementation of the sliding window metrics:10%

Training on MS lesion segmentation task: 30%

Extension to other datasets: 20%

Results analysis: 20%

Fig. Results of an existing lesion filling algorithm, showing inadequate performance

Requirements:

Interest/Experience with image processing

Python programming knowledge (Pytorch bonus)

Interest in neuroimaging

Supervisor(s):

PD. Dr. Richard McKinley

Institutes: Diagnostic and Interventional Neuroradiology

Center for Artificial Intelligence in Medicine (CAIM), University of Bern

References: [1] Brain Imaging Generation with Latent Diffusion Models , Pinaya et al, Accepted in the Deep Generative Models workshop @ MICCAI 2022 , https://arxiv.org/abs/2209.07162

Contact : PD Dr Richard McKinley, Support Centre for Advanced Neuroimaging ( [email protected] )

Improving metrics and loss functions for targets with imbalanced size: sliding window Dice coefficient and loss.

Background The Dice coefficient is the most commonly used metric for segmentation quality in medical imaging, and a differentiable version of the coefficient is often used as a loss function, in particular for small target classes such as multiple sclerosis lesions. Dice coefficient has the benefit that it is applicable in instances where the target class is in the minority (for example, in case of segmenting small lesions). However, if lesion sizes are mixed, the loss and metric is biased towards performance on large lesions, leading smaller lesions to be missed and harming overall lesion detection. A recently proposed loss function (blob loss[1]) aims to combat this by treating each connected component of a lesion mask separately, and claims improvements over Dice loss on lesion detection scores in a variety of tasks.

Aim: The aim of this thesisis twofold. First, to benchmark blob loss against a simple, potentially superior loss for instance detection: sliding window Dice loss, in which the Dice loss is calculated over a sliding window across the area/volume of the medical image. Second, we will investigate whether a sliding window Dice coefficient is better corellated with lesion-wise detection metrics than Dice coefficient and may serve as an alternative metric capturing both global and instance-wise detection.

Materials and Methods: A large, annotated, heterogeneous dataset of MRI data from MS patients will be available for benchmarking the method, as well as our existing codebases for MS lesion segmentation. Extension of the method to other diseases and datasets (such as covered in the blob loss paper) will make the method more plausible for publication. The student will work alongside clinicians and engineers carrying out research in multiple sclerosis lesion segmentation, in particular in the context of our running project supported by the CAIM grant.

Fig. An annotated MS lesion case, showing the variety of lesion sizes

References: [1] blob loss: instance imbalance aware loss functions for semantic segmentation, Kofler et al, https://arxiv.org/abs/2205.08209

Idempotent and partial skull-stripping in multispectral MRI imaging

Background Skull stripping (or brain extraction) refers to the masking of non-brain tissue from structural MRI imaging. Since 3D MRI sequences allow reconstruction of facial features, many data providers supply data only after skull-stripping, making this a vital tool in data sharing. Furthermore, skull-stripping is an important pre-processing step in many neuroimaging pipelines, even in the deep-learning era: while many methods could now operate on data with skull present, they have been trained only on skull-stripped data and therefore produce spurious results on data with the skull present.

High-quality skull-stripping algorithms based on deep learning are now widely available: the most prominent example is HD-BET [1]. A major downside of HD-BET is its behaviour on datasets to which skull-stripping has already been applied: in this case the algorithm falsely identifies brain tissue as skull and masks it. A skull-stripping algorithm F not exhibiting this behaviour would be idempotent: F(F(x)) = F(x) for any image x. Furthermore, legacy datasets from before the availability of high-quality skull-stripping algorithms may still contain images which have been inadequately skull-stripped: currently the only solution to improve the skull-stripping on this data is to go back to the original datasource or to manually correct the skull-stripping, which is time-consuming and prone to error.

Aim: In this project, the student will develop an idempotent skull-stripping network which can also handle partially skull-stripped inputs. In the best case, the network will operate well on a large subset of the data we work with (e.g. structural MRI, diffusion-weighted MRI, Perfusion-weighted MRI, susceptibility-weighted MRI, at a variety of field strengths) to maximize the future applicability of the network across the teams in our group.

Materials and Methods: Multiple datasets, both publicly available and internal (encompassing thousands of 3D volumes) will be available. Silver standard reference data for standard sequences at 1.5T and 3T can be generated using existing tools such as HD-BET: for other sequences and field strengths semi-supervised learning or methods improving robustness to domain shift may be employed. Robustness to partial skull-stripping may be induced by a combination of learning theory and model-based approaches.

Dataset curation: 10%

Idempotent skull-stripping model building: 30%

Modelling of partial skull-stripping:10%

Extension of model to handle partial skull: 30%

Results analysis: 10%

Fig. An example of failed skull-stripping requiring manual correction

References: [1] Isensee, F, Schell, M, Pflueger, I, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp . 2019; 40: 4952– 4964. https://doi.org/10.1002/hbm.24750

Automated leaf detection and leaf area estimation (for Arabidopsis thaliana)

Correlating plant phenotypes such as leaf area or number of leaves to the genotype (i.e. changes in DNA) is a common goal for plant breeders and molecular biologists. Such data can not only help to understand fundamental processes in nature, but also can help to improve ecotypes, e.g., to perform better under climate change, or reduce fertiliser input. However, collecting data for many plants is very time consuming and automated data acquisition is necessary.

The project aims at building a machine learning model to automatically detect plants in top-view images (see examples below), segment their leaves (see Fig C) and to estimate the leaf area. This information will then be used to determine the leaf area of different Arabidopsis ecotypes. The project will be carried out in collaboration with researchers of the Institute of Plant Sciences at the University of Bern. It will also involve the design and creation of a dataset of plant top-views with the corresponding annotation (provided by experts at the Institute of Plant Sciences).

Contact: Prof. Dr. Paolo Favaro ( [email protected] )

Master Projects at the ARTORG Center

The Gerontechnology and Rehabilitation group at the ARTORG Center for Biomedical Engineering is offering multiple MSc thesis projects to students, which are interested in working with real patient data, artificial intelligence and machine learning algorithms. The goal of these projects is to transfer the findings to the clinic in order to solve today’s healthcare problems and thus to improve the quality of life of patients. Assessment of Digital Biomarkers at Home by Radar. [PDF] Comparison of Radar, Seismograph and Ballistocardiography and to Monitor Sleep at Home. [PDF] Sentimental Analysis in Speech. [PDF] Contact: Dr. Stephan Gerber ( [email protected] )

Internship in Computational Imaging at Prophesee

A 6 month intership at Prophesee, Grenoble is offered to a talented Master Student.

The topic of the internship is working on burst imaging following the work of Sam Hasinoff , and exploring ways to improve it using event-based vision.

A compensation to cover the expenses of living in Grenoble is offered. Only students that have legal rights to work in France can apply.

Anyone interested can send an email with the CV to Daniele Perrone ( [email protected] ).

Using machine learning applied to wearables to predict mental health

This Master’s project lies at the intersection of psychiatry and computer science and aims to use machine learning techniques to improve health. Using sensors to detect sleep and waking behavior has as of yet unexplored potential to reveal insights into health. In this study, we make use of a watch-like device, called an actigraph, which tracks motion to quantify sleep behavior and waking activity. Participants in the study consist of healthy and depressed adolescents and wear actigraphs for a year during which time we query their mental health status monthly using online questionnaires. For this masters thesis we aim to make use of machine learning methods to predict mental health based on the data from the actigraph. The ability to predict mental health crises based on sleep and wake behavior would provide an opportunity for intervention, significantly impacting the lives of patients and their families. This Masters thesis is a collaboration between Professor Paolo Favaro at the Institute of Computer Science ( [email protected] ) and Dr Leila Tarokh at the Universitäre Psychiatrische Dienste (UPD) ( [email protected] ). We are looking for a highly motivated individual interested in bridging disciplines.

Bachelor or Master Projects at the ARTORG Center

The Gerontechnology and Rehabilitation group at the ARTORG Center for Biomedical Engineering is offering multiple BSc- and MSc thesis projects to students, which are interested in working with real patient data, artificial intelligence and machine learning algorithms. The goal of these projects is to transfer the findings to the clinic in order to solve today’s healthcare problems and thus to improve the quality of life of patients. Machine Learning Based Gait-Parameter Extraction by Using Simple Rangefinder Technology. [PDF] Detection of Motion in Video Recordings [PDF] Home-Monitoring of Elderly by Radar [PDF] Gait feature detection in Parkinson's Disease [PDF] Development of an arthroscopic training device using virtual reality [PDF] Contact: Dr. Stephan Gerber ( [email protected] ), Michael Single ( [email protected]. ch )

Dynamic Transformer

Level: bachelor.

Visual Transformers have obtained state of the art classification accuracies [ViT, DeiT, T2T, BoTNet]. Mixture of experts could be used to increase the capacity of a neural network by learning instance dependent execution pathways in a network [MoE]. In this research project we aim to push the transformers to their limit and combine their dynamic attention with MoEs, compared to Switch Transformer [Switch], we will use a much more efficient formulation of mixing [CondConv, DynamicConv] and we will use this idea in the attention part of the transformer, not the fully connected layer.

Input dependent attention kernel generation for better transformer layers.

Publication Opportunity: Dynamic Neural Networks Meets Computer Vision (a CVPR 2021 Workshop)

Extensions:

The same idea could be extended to other ViT/Transformer based models [DETR, SETR, LSTR, TrackFormer, BERT]

Quantized ViT

Visual Transformers have obtained state of the art classification accuracies [ViT, CLIP, DeiT], but the best ViT models are extremely compute heavy and running them even only for inference (not doing backpropagation) is expensive. Running transformers cheaply by quantization is not a new problem and it has been tackled before for BERT [BERT] in NLP [Q-BERT, Q8BERT, TernaryBERT, BinaryBERT]. In this project we will be trying to quantize pretrained ViT models.

Quantizing ViT models for faster inference and smaller models without losing accuracy

Publication Opportunity: Binary Networks for Computer Vision 2021 (a CVPR workshop)

Extensions:

Having a fast pipeline for image inference with ViT will allow us to dig deep into the attention of ViT and analyze it, we might be able to prune some attention heads or replace them with static patterns (like local convolution or dilated patterns), We might be even able to replace the transformer with performer and increase the throughput even more [Performer].
The same idea could be extended to other ViT based models [DETR, SETR, LSTR, TrackFormer, CPTR, BoTNet, T2TViT]
Learning Transferable Visual Models From Natural Language Supervision [CLIP]
Visual Transformers: Token-based Image Representation and Processing for Computer Vision [ViT]
DeiT: Data-efficient Image Transformers [DeiT]
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [BERT]
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT [Q-BERT]
Q8BERT: Quantized 8Bit BERT [Q8BERT]
TernaryBERT: Distillation-aware Ultra-low Bit BERT [TernaryBERT]
BinaryBERT: Pushing the Limit of BERT Quantization [BinaryBERT]
Rethinking Attention with Performers [Performer]
End-to-End Object Detection with Transformers [DETR]
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [SETR]
End-to-end Lane Shape Prediction with Transformers [LSTR]
TrackFormer: Multi-Object Tracking with Transformers [TrackFormer]
CPTR: Full Transformer Network for Image Captioning [CPTR]
Bottleneck Transformers for Visual Recognition [BoTNet]
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [T2TViT]

Multimodal Contrastive Learning

Recently contrastive learning has gained a lot of attention for self-supervised image representation learning [SimCLR, MoCo]. Contrastive learning could be extended to multimodal data, like videos (images and audio) [CMC, CoCLR]. Most contrastive methods require large batch sizes (or large memory pools) which makes them expensive for training. In this project we are going to use non batch size dependent contrastive methods [SwAV, BYOL, SimSiam] to train multimodal representation extractors.

Our main goal is to compare the proposed method with the CMC baseline, so we will be working with STL10, ImageNet, UCF101, HMDB51, and NYU Depth-V2 datasets.

Inspired by the recent works on smaller datasets [ConVIRT, CPD], to accelerate the training speed, we could start with two pretrained single-modal models and finetune them with the proposed method.

Extending SwAV to multimodal datasets
Grasping a better understanding of the BYOL

Publication Opportunity: MULA 2021 (a CVPR workshop on Multimodal Learning and Applications)

Most knowledge distillation methods for contrastive learners also use large batch sizes (or memory pools) [CRD, SEED], the proposed method could be extended for knowledge distillation.
One could easily extend this idea to multiview learning, for example one could have two different networks working on the same input and train them with contrastive learning, this may lead to better models [DeiT] by cross-model inductive biases communications.
Self-supervised Co-training for Video Representation Learning [CoCLR]
Learning Spatiotemporal Features via Video and Text Pair Discrimination [CPD]
Audio-Visual Instance Discrimination with Cross-Modal Agreement [AVID-CMA]
Self-Supervised Learning by Cross-Modal Audio-Video Clustering [XDC]
Contrastive Multiview Coding [CPC]
Contrastive Learning of Medical Visual Representations from Paired Images and Text [ConVIRT]
A Simple Framework for Contrastive Learning of Visual Representations [SimCLR]
Momentum Contrast for Unsupervised Visual Representation Learning [MoCo]
Bootstrap your own latent: A new approach to self-supervised Learning [BYOL]
Exploring Simple Siamese Representation Learning [SimSiam]
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments [SwAV]
Contrastive Representation Distillation [CRD]
SEED: Self-supervised Distillation For Visual Representation [SEED]

Robustness of Neural Networks

Neural Networks have been found to achieve surprising performance in several tasks such as classification, detection and segmentation. However, they are also very sensitive to small (controlled) changes to the input. It has been shown that some changes to an image that are not visible to the naked eye may lead the network to output an incorrect label. This thesis will focus on studying recent progress in this area and aim to build a procedure for a trained network to self-assess its reliability in classification or one of the popular computer vision tasks.

Contact: Paolo Favaro

Masters projects at sitem center

The Personalised Medicine Research Group at the sitem Center for Translational Medicine and Biomedical Entrepreneurship is offering multiple MSc thesis projects to the biomed eng MSc students that may also be of interest to the computer science students. Automated quantification of cartilage quality for hip treatment decision support. PDF Automated quantification of massive rotator cuff tears from MRI. PDF Deep learning-based segmentation and fat fraction analysis of the shoulder muscles using quantitative MRI. PDF Unsupervised Domain Adaption for Cross-Modality Hip Joint Segmentation. PDF Contact: Dr. Kate Gerber

Internships/Master thesis @ Chronocam

3-6 months internships on event-based computer vision. Chronocam is a rapidly growing startup developing event-based technology, with more than 15 PhDs working on problems like tracking, detection, classification, SLAM, etc. Event-based computer vision has the potential to solve many long-standing problems in traditional computer vision, and this is a super exciting time as this potential is becoming more and more tangible in many real-world applications. For next year we are looking for motivated Master and PhD students with good software engineering skills (C++ and/or python), and preferable good computer vision and deep learning background. PhD internships will be more research focused and possibly lead to a publication. For each intern we offer a compensation to cover the expenses of living in Paris. List of some of the topics we want to explore:

Photo-realistic image synthesis and super-resolution from event-based data (PhD)
Self-supervised representation learning (PhD)
End-to-end Feature Learning for Event-based Data
Bio-inspired Filtering using Spiking Networks
On-the fly Compression of Event-based Streams for Low-Power IoT Cameras
Tracking of Multiple Objects with a Dual-Frequency Tracker
Event-based Autofocus
Stabilizing an Event-based Stream using an IMU
Crowd Monitoring for Low-power IoT Cameras
Road Extraction from an Event-based Camera Mounted in a Car for Autonomous Driving
Sign detection from an Event-based Camera Mounted in a Car for Autonomous Driving
High-frequency Eye Tracking

Email with attached CV to Daniele Perrone at [email protected] .

Contact: Daniele Perrone

Object Detection in 3D Point Clouds

Today we have many 3D scanning techniques that allow us to capture the shape and appearance of objects. It is easier than ever to scan real 3D objects and transform them into a digital model for further processing, such as modeling, rendering or animation. However, the output of a 3D scanner is often a raw point cloud with little to no annotations. The unstructured nature of the point cloud representation makes it difficult for processing, e.g. surface reconstruction. One application is the detection and segmentation of an object of interest. In this project, the student is challenged to design a system that takes a point cloud (a 3D scan) as input and outputs the names of objects contained in the scan. This output can then be used to eliminate outliers or points that belong to the background. The approach involves collecting a large dataset of 3D scans and training a neural network on it.

Contact: Adrian Wälchli

Shape Reconstruction from a Single RGB Image or Depth Map

A photograph accurately captures the world in a moment of time and from a specific perspective. Since it is a projection of the 3D space to a 2D image plane, the depth information is lost. Is it possible to restore it, given only a single photograph? In general, the answer is no. This problem is ill-posed, meaning that many different plausible depth maps exist, and there is no way of telling which one is the correct one. However, if we cover one of our eyes, we are still able to recognize objects and estimate how far away they are. This motivates the exploration of an approach where prior knowledge can be leveraged to reduce the ill-posedness of the problem. Such a prior could be learned by a deep neural network, trained with many images and depth maps.

CNN Based Deblurring on Mobile

Deblurring finds many applications in our everyday life. It is particularly useful when taking pictures on handheld devices (e.g. smartphones) where camera shake can degrade important details. Therefore, it is desired to have a good deblurring algorithm implemented directly in the device. In this project, the student will implement and optimize a state-of-the-art deblurring method based on a deep neural network for deployment on mobile phones (Android). The goal is to reduce the number of network weights in order to reduce the memory footprint while preserving the quality of the deblurred images. The result will be a camera app that automatically deblurs the pictures, giving the user a choice of keeping the original or the deblurred image.

Depth from Blur

If an object in front of the camera or the camera itself moves while the aperture is open, the region of motion becomes blurred because the incoming light is accumulated in different positions across the sensor. If there is camera motion, there is also parallax. Thus, a motion blurred image contains depth information. In this project, the student will tackle the problem of recovering a depth-map from a motion-blurred image. This includes the collection of a large dataset of blurred- and sharp images or videos using a pair or triplet of GoPro action cameras. Two cameras will be used in stereo to estimate the depth map, and the third captures the blurred frames. This data is then used to train a convolutional neural network that will predict the depth map from the blurry image.

Unsupervised Clustering Based on Pretext Tasks

The idea of this project is that we have two types of neural networks that work together: There is one network A that assigns images to k clusters and k (simple) networks of type B perform a self-supervised task on those clusters. The goal of all the networks is to make the k networks of type B perform well on the task. The assumption is that clustering in semantically similar groups will help the networks of type B to perform well. This could be done on the MNIST dataset with B being linear classifiers and the task being rotation prediction.

Adversarial Data-Augmentation

The student designs a data augmentation network that transforms training images in such a way that image realism is preserved (e.g. with a constrained spatial transformer network) and the transformed images are more difficult to classify (trained via adversarial loss against an image classifier). The model will be evaluated for different data settings (especially in the low data regime), for example on the MNIST and CIFAR datasets.

Unsupervised Learning of Lip-reading from Videos

People with sensory impairment (hearing, speech, vision) depend heavily on assistive technologies to communicate and navigate in everyday life. The mass production of media content today makes it impossible to manually translate everything into a common language for assistive technologies, e.g. captions or sign language. In this project, the student employs a neural network to learn a representation for lip-movement in videos in an unsupervised fashion, possibly with an encoder-decoder structure where the decoder reconstructs the audio signal. This requires collecting a large dataset of videos (e.g. from YouTube) of speakers or conversations where lip movement is visible. The outcome will be a neural network that learns an audio-visual representation of lip movement in videos, which can then be leveraged to generate captions for hearing impaired persons.

Learning to Generate Topographic Maps from Satellite Images

Satellite images have many applications, e.g. in meteorology, geography, education, cartography and warfare. They are an accurate and detailed depiction of the surface of the earth from above. Although it is relatively simple to collect many satellite images in an automated way, challenges arise when processing them for use in navigation and cartography. The idea of this project is to automatically convert an arbitrary satellite image, of e.g. a city, to a map of simple 2D shapes (streets, houses, forests) and label them with colors (semantic segmentation). The student will collect a dataset of satellite image and topological maps and train a deep neural network that learns to map from one domain to the other. The data could be obtained from a Google Maps database or similar.

New Variables of Brain Morphometry: the Potential and Limitations of CNN Regression

Timo blattner · sept. 2022.

The calculation of variables of brain morphology is computationally very expensive and time-consuming. A previous work showed the feasibility of ex- tracting the variables directly from T1-weighted brain MRI images using a con- volutional neural network. We used significantly more data and extended their model to a new set of neuromorphological variables, which could become inter- esting biomarkers in the future for the diagnosis of brain diseases. The model shows for nearly all subjects a less than 5% mean relative absolute error. This high relative accuracy can be attributed to the low morphological variance be- tween subjects and the ability of the model to predict the cortical atrophy age trend. The model however fails to capture all the variance in the data and shows large regional differences. We attribute these limitations in part to the moderate to poor reliability of the ground truth generated by FreeSurfer. We further investigated the effects of training data size and model complexity on this regression task and found that the size of the dataset had a significant impact on performance, while deeper models did not perform better. Lack of interpretability and dependence on a silver ground truth are the main drawbacks of this direct regression approach.

Home Monitoring by Radar

Lars ziegler · sept. 2022.

Detection and tracking of humans via UWB radars is a promising and continuously evolving field with great potential for medical technology. This contactless method of acquiring data of a patients movement patterns is ideal for in home application. As irregularities in a patients movement patterns are an indicator for various health problems including neurodegenerative diseases, the insight this data could provide may enable earlier detection of such problems. In this thesis a signal processing pipeline is presented with which a persons movement is modeled. During an experiment 142 measurements were recorded by two separate radar systems and one lidar system which each consisted of multiple sensors. The models that were calculated on these measurements by the signal processing pipeline were used to predict the times when a person stood up or sat down. The predictions showed an accuracy of 72.2%.

Revisiting non-learning based 3D reconstruction from multiple images

Aaron sägesser · oct. 2021.

Arthroscopy consists of challenging tasks and requires skills that even today, young surgeons still train directly throughout the surgery. Existing simulators are expensive and rarely available. Through the growing potential of virtual reality(VR) (head-mounted) devices for simulation and their applicability in the medical context, these devices have become a promising alternative that would be orders of magnitude cheaper and could be made widely available. To build a VR-based training device for arthroscopy is the overall aim of our project, as this would be of great benefit and might even be applicable in other minimally invasive surgery (MIS). This thesis marks a first step of the project with its focus to explore and compare well-known algorithms in a multi-view stereo (MVS) based 3D reconstruction with respect to imagery acquired by an arthroscopic camera. Simultaneously with this reconstruction, we aim to gain essential measures to compare the VR environment to the real world, as validation of the realism of future VR tasks. We evaluate 3 different feature extraction algorithms with 3 different matching techniques and 2 different algorithms for the estimation of the fundamental (F) matrix. The evaluation of these 18 different setups is made with a reconstruction pipeline embedded in a jupyter notebook implemented in python based on common computer vision libraries and compared with imagery generated with a mobile phone as well as with the reconstruction results of state-of-the-art (SOTA) structure-from-motion (SfM) software COLMAP and Multi-View Environment (MVE). Our comparative analysis manifests the challenges of heavy distortion, the fish-eye shape and weak image quality of arthroscopic imagery, as all results are substantially worse using this data. However, there are huge differences regarding the different setups. Scale Invariant Feature Transform (SIFT) and Oriented FAST Rotated BRIEF (ORB) in combination with k-Nearest Neighbour (kNN) matching and Least Median of Squares (LMedS) present the most promising results. Overall, the 3D reconstruction pipeline is a useful tool to foster the process of gaining measurements from the arthroscopic exploration device and to complement the comparative research in this context.

Examination of Unsupervised Representation Learning by Predicting Image Rotations

Eric lagger · sept. 2020.

In recent years deep convolutional neural networks achieved a lot of progress. To train such a network a lot of data is required and in supervised learning algorithms it is necessary that the data is labeled. To label data there is a lot of human work needed and this takes a lot of time and money to be done. To avoid the inconveniences that come with this we would like to find systems that don’t need labeled data and therefore are unsupervised learning algorithms. This is the importance of unsupervised algorithms, even though their outcome is not yet on the same qualitative level as supervised algorithms. In this thesis we will discuss an approach of such a system and compare the results to other papers. A deep convolutional neural network is trained to learn the rotations that have been applied to a picture. So we take a large amount of images and apply some simple rotations and the task of the network is to discover in which direction the image has been rotated. The data doesn’t need to be labeled to any category or anything else. As long as all the pictures are upside down we hope to find some high dimensional patterns for the network to learn.

StitchNet: Image Stitching using Autoencoders and Deep Convolutional Neural Networks

Maurice rupp · sept. 2019.

This thesis explores the prospect of artificial neural networks for image processing tasks. More specifically, it aims to achieve the goal of stitching multiple overlapping images to form a bigger, panoramic picture. Until now, this task is solely approached with ”classical”, hardcoded algorithms while deep learning is at most used for specific subtasks. This thesis introduces a novel end-to-end neural network approach to image stitching called StitchNet, which uses a pre-trained autoencoder and deep convolutional networks. Additionally to presenting several new datasets for the task of supervised image stitching with each 120’000 training and 5’000 validation samples, this thesis also conducts various experiments with different kinds of existing networks designed for image superresolution and image segmentation adapted to the task of image stitching. StitchNet outperforms most of the adapted networks in both quantitative as well as qualitative results.

Facial Expression Recognition in the Wild

Luca rolshoven · sept. 2019.

The idea of inferring the emotional state of a subject by looking at their face is nothing new. Neither is the idea of automating this process using computers. Researchers used to computationally extract handcrafted features from face images that had proven themselves to be effective and then used machine learning techniques to classify the facial expressions using these features. Recently, there has been a trend towards using deeplearning and especially Convolutional Neural Networks (CNNs) for the classification of these facial expressions. Researchers were able to achieve good results on images that were taken in laboratories under the same or at least similar conditions. However, these models do not perform very well on more arbitrary face images with different head poses and illumination. This thesis aims to show the challenges of Facial Expression Recognition (FER) in this wild setting. It presents the currently used datasets and the present state-of-the-art results on one of the biggest facial expression datasets currently available. The contributions of this thesis are twofold. Firstly, I analyze three famous neural network architectures and their effectiveness on the classification of facial expressions. Secondly, I present two modifications of one of these networks that lead to the proposed STN-COV model. While this model does not outperform all of the current state-of-the-art models, it does beat several ones of them.

A Study of 3D Reconstruction of Varying Objects with Deformable Parts Models

Raoul grossenbacher · july 2019.

This work covers a new approach to 3D reconstruction. In traditional 3D reconstruction one uses multiple images of the same object to calculate a 3D model by taking information gained from the differences between the images, like camera position, illumination of the images, rotation of the object and so on, to compute a point cloud representing the object. The characteristic trait shared by all these approaches is that one can almost change everything about the image, but it is not possible to change the object itself, because one needs to find correspondences between the images. To be able to use different instances of the same object, we used a 3D DPM model that can find different parts of an object in an image, thereby detecting the correspondences between the different pictures, which we then can use to calculate the 3D model. To take this theory to practise, we gave a 3D DPM model, which was trained to detect cars, pictures of different car brands, where no pair of images showed the same vehicle and used the detected correspondences and the Factorization Method to compute the 3D point cloud. This technique leads to a completely new approach in 3D reconstruction, because changing the object itself was never done before.

Motion deblurring in the wild replication and improvements

Alvaro juan lahiguera · jan. 2019, coma outcome prediction with convolutional neural networks, stefan jonas · oct. 2018, automatic correction of self-introduced errors in source code, sven kellenberger · aug. 2018, neural face transfer: training a deep neural network to face-swap, till nikolaus schnabel · july 2018.

This thesis explores the field of artificial neural networks with realistic looking visual outputs. It aims at morphing face pictures of a specific identity to look like another individual by only modifying key features, such as eye color, while leaving identity-independent features unchanged. Prior works have covered the topic of symmetric translation between two specific domains but failed to optimize it on faces where only parts of the image may be changed. This work applies a face masking operation to the output at training time, which forces the image generator to preserve colors while altering the face, fitting it naturally inside the unmorphed surroundings. Various experiments are conducted including an ablation study on the final setting, decreasing the baseline identity switching performance from 81.7% to 75.8 % whilst improving the average χ2 color distance from 0.551 to 0.434. The provided code-based software gives users easy access to apply this neural face swap to images and videos of arbitrary crop and brings Computer Vision one step closer to replacing Computer Graphics in this specific area.

A Study of the Importance of Parts in the Deformable Parts Model

Sammer puran · june 2017, self-similarity as a meta feature, lucas husi · april 2017, a study of 3d deformable parts models for detection and pose-estimation, simon jenni · march 2015, accelerated federated learning on client silos with label noise: rho selection in classification and segmentation, irakli kelbakiani · may 2024.

Federated Learning has recently gained more research interest. This increased attention is caused by factors including the growth of decentralized data, privacy concerns, and new privacy regulations. In Federated Learning, remote servers keep training a model on local datasets independently, and subsequently, local models are aggregated into a global model, which achieves better overall performance. Sending local model weights instead of the entire dataset is a significant advantage of Federated Learning over centralized classical machine learning algorithms. Federated learning involves uploading and downloading model parameters multiple times, so there are multiple communication rounds between the global server and remote client servers, which imposes challenges. The high number of necessary communication rounds not only increases high-cost communication overheads but is also a critical limitation for servers with low network bandwidth, which leads to latency and a higher probability of training failures caused by communication breakdowns. To mitigate these challenges, we aim to provide a fast-convergent Federated Learning training methodology that decreases the number of necessary communication rounds. We found a paper about Reducible Holdout Loss Selection (RHO-Loss) batch selection methodology, which ”selects low-noise, task-relevant, non-redundant points for training” [1]; we hypothesize, if client silos employ RHO-Loss methodology and successfully avoid training their local models on noisy and non-relevant samples, clients may offer stable and consistent updates to the global server, which could lead to faster convergence of the global model. Our contribution focuses on investigating the RHO-Loss method in a simulated federated setting for the Clothing1M dataset. We also examine its applicability to medical datasets and check its effectiveness in a simulated federated environment. Our experimental results show a promising outcome, specifically a reduction in communication rounds for the Clothing1M dataset. However, as the success of the RHO-Loss selection method depends on the availability of sufficient training data for the target RHO model and for the Irreducible RHO model, we emphasize that our contribution applies to those Federated Learning scenarios where client silos hold enough training data to successfully train and benefit from their RHO model on their local dataset.

Amodal Leaf Segmentation

Nicolas maier · nov. 2023.

Plant phenotyping is the process of measuring and analyzing various traits of plants. It provides essential information on how genetic and environmental factors affect plant growth and development. Manual phenotyping is highly time-consuming; therefore, many computer vision and machine learning based methods have been proposed in the past years to perform this task automatically based on images of the plants. However, the publicly available datasets (in particular, of Arabidopsis thaliana) are limited in size and diversity, making them unsuitable to generalize to new unseen environments. In this work, we propose a complete pipeline able to automatically extract traits of interest from an image of Arabidopsis thaliana. Our method uses a minimal amount of existing annotated data from a source domain to generate a large synthetic dataset adapted to a different target domain (e.g., different backgrounds, lighting conditions, and plant layouts). In addition, unlike the source dataset, the synthetic one provides ground-truth annotations for the occluded parts of the leaves, which are relevant when measuring some characteristics of the plant, e.g., its total area. This synthetic dataset is then used to train a model to perform amodal instance segmentation of the leaves to obtain the total area, leaf count, and color of each plant. To validate our approach, we create a small dataset composed of manually annotated real images of Arabidopsis thaliana, which is used to assess the performance of the models.

Assessment of movement and pose in a hospital bed by ambient and wearable sensor technology in healthy subjects

Tony licata · sept. 2022.

The use of automated systems describing the human motion has become possible in various domains. Most of the proposed systems are designed to work with people moving around in a standing position. Because such system could be interesting in a medical environment, we propose in this work a pipeline that can effectively predict human motion from people lying on beds. The proposed pipeline is tested with a data set composed of 41 participants executing 7 predefined tasks in a bed. The motion of the participants is measured with video cameras, accelerometers and pressure mat. Various experiments are carried with the information retrieved from the data set. Two approaches combining the data from the different measure technologies are explored. The performance of the different carried experiments is measured, and the proposed pipeline is composed with components providing the best results. Later on, we show that the proposed pipeline only needs to use the video cameras, which make the proposed environment easier to implement in real life situations.

Machine Learning Based Prediction of Mental Health Using Wearable-measured Time Series

Seyedeh sharareh mirzargar · sept. 2022.

Depression is the second major cause for years spent in disability and has a growing prevalence in adolescents. The recent Covid-19 pandemic has intensified the situation and limited in-person patient monitoring due to distancing measures. Recent advances in wearable devices have made it possible to record the rest/activity cycle remotely with high precision and in real-world contexts. We aim to use machine learning methods to predict an individual's mental health based on wearable-measured sleep and physical activity. Predicting an impending mental health crisis of an adolescent allows for prompt intervention, detection of depression onset or its recursion, and remote monitoring. To achieve this goal, we train three primary forecasting models; linear regression, random forest, and light gradient boosted machine (LightGBM); and two deep learning models; block recurrent neural network (block RNN) and temporal convolutional network (TCN); on Actigraph measurements to forecast mental health in terms of depression, anxiety, sleepiness, stress, sleep quality, and behavioral problems. Our models achieve a high forecasting performance, the random forest being the winner to reach an accuracy of 98% for forecasting the trait anxiety. We perform extensive experiments to evaluate the models' performance in accuracy, generalization, and feature utilization, using a naive forecaster as the baseline. Our analysis shows minimal mental health changes over two months, making the prediction task easily achievable. Due to these minimal changes in mental health, the models tend to primarily use the historical values of mental health evaluation instead of Actigraph features. At the time of this master thesis, the data acquisition step is still in progress. In future work, we plan to train the models on the complete dataset using a longer forecasting horizon to increase the level of mental health changes and perform transfer learning to compensate for the small dataset size. This interdisciplinary project demonstrates the opportunities and challenges in machine learning based prediction of mental health, paving the way toward using the same techniques to forecast other mental disorders such as internalizing disorder, Parkinson's disease, Alzheimer's disease, etc. and improving the quality of life for individuals who have some mental disorder.

CNN Spike Detector: Detection of Spikes in Intracranial EEG using Convolutional Neural Networks

Stefan jonas · oct. 2021.

The detection of interictal epileptiform discharges in the visual analysis of electroencephalography (EEG) is an important but very difficult, tedious, and time-consuming task. There have been decades of research on computer-assisted detection algorithms, most recently focused on using Convolutional Neural Networks (CNNs). In this thesis, we present the CNN Spike Detector, a convolutional neural network to detect spikes in intracranial EEG. Our dataset of 70 intracranial EEG recordings from 26 subjects with epilepsy introduces new challenges in this research field. We report cross-validation results with a mean AUC of 0.926 (+- 0.04), an area under the precision-recall curve (AUPRC) of 0.652 (+- 0.10) and 12.3 (+- 7.47) false positive epochs per minute for a sensitivity of 80%. A visual examination of false positive segments is performed to understand the model behavior leading to a relatively high false detection rate. We notice issues with the evaluation measures and highlight a major limitation of the common approach of detecting spikes using short segments, namely that the network is not capable to consider the greater context of the segment with regards to its origination. For this reason, we present the Context Model, an extension in which the CNN Spike Detector is supplied with additional information about the channel. Results show promising but limited performance improvements. This thesis provides important findings about the spike detection task for intracranial EEG and lays out promising future research directions to develop a network capable of assisting experts in real-world clinical applications.

PolitBERT - Deepfake Detection of American Politicians using Natural Language Processing

Maurice rupp · april 2021.

This thesis explores the application of modern Natural Language Processing techniques to the detection of artificially generated videos of popular American politicians. Instead of focusing on detecting anomalies and artifacts in images and sounds, this thesis focuses on detecting irregularities and inconsistencies in the words themselves, opening up a new possibility to detect fake content. A novel, domain-adapted, pre-trained version of the language model BERT combined with several mechanisms to overcome severe dataset imbalances yielded the best quantitative as well as qualitative results. Additionally to the creation of the biggest publicly available dataset of English-speaking politicians consisting of 1.5 M sentences from over 1000 persons, this thesis conducts various experiments with different kinds of text classification and sequence processing algorithms applied to the political domain. Furthermore, multiple ablations to manage severe data imbalance are presented and evaluated.

A Study on the Inversion of Generative Adversarial Networks

Ramona beck · march 2021.

The desire to use generative adversarial networks (GANs) for real-world tasks such as object segmentation or image manipulation is increasing as synthesis quality improves, which has given rise to an emerging research area called GAN inversion that focuses on exploring methods for embedding real images into the latent space of a GAN. In this work, we investigate different GAN inversion approaches using an existing generative model architecture that takes a completely unsupervised approach to object segmentation and is based on StyleGAN2. In particular, we propose and analyze algorithms for embedding real images into the different latent spaces Z, W, and W+ of StyleGAN following an optimization-based inversion approach, while also investigating a novel approach that allows fine-tuning of the generator during the inversion process. Furthermore, we investigate a hybrid and a learning-based inversion approach, where in the former we train an encoder with embeddings optimized by our best optimization-based inversion approach, and in the latter we define an autoencoder, consisting of an encoder and the generator of our generative model as a decoder, and train it to map an image into the latent space. We demonstrate the effectiveness of our methods as well as their limitations through a quantitative comparison with existing inversion methods and by conducting extensive qualitative and quantitative experiments with synthetic data as well as real images from a complex image dataset. We show that we achieve qualitatively satisfying embeddings in the W and W+ spaces with our optimization-based algorithms, that fine-tuning the generator during the inversion process leads to qualitatively better embeddings in all latent spaces studied, and that the learning-based approach also benefits from a variable generator as well as a pre-training with our hybrid approach. Furthermore, we evaluate our approaches on the object segmentation task and show that both our optimization-based and our hybrid and learning-based methods are able to generate meaningful embeddings that achieve reasonable object segmentations. Overall, our proposed methods illustrate the potential that lies in the GAN inversion and its application to real-world tasks, especially in the relaxed version of the GAN inversion where the weights of the generator are allowed to vary.

Multi-scale Momentum Contrast for Self-supervised Image Classification

Zhao xueqi · dec. 2020.

With the maturity of supervised learning technology, people gradually shift the research focus to the field of self-supervised learning. ”Momentum Contrast” (MoCo) proposes a new self-supervised learning method and raises the correct rate of self-supervised learning to a new level. Inspired by another article ”Representation Learning by Learning to Count”, if a picture is divided into four parts and passed through a neural network, it is possible to further improve the accuracy of MoCo. Different from the original MoCo, this MoCo variant (Multi-scale MoCo) does not directly pass the image through the encoder after the augmented images. Multi-scale MoCo crops and resizes the augmented images, and the obtained four parts are respectively passed through the encoder and then summed (upsampled version do not do resize to input but resize the contrastive samples). This method of images crop is not only used for queue q but also used for comparison queue k, otherwise the weights of queue k might be damaged during the moment update. This will further discussed in the experiments chapter between downsampled Multi-scale version and downsampled both Multi-scale version. Human beings also have the same principle of object recognition: when human beings see something they are familiar with, even if the object is not fully displayed, people can still guess the object itself with a high probability. Because of this, Multi-scale MoCo applies this concept to the pretext part of MoCo, hoping to obtain better feature extraction. In this thesis, there are three versions of Multi-scale MoCo, downsampled input samples version, downsampled input samples and contrast samples version and upsampled input samples version. The differences between these versions will be described in more detail later. The neural network architecture comparison includes ResNet50 , and the tested data set is STL-10. The weights obtained in pretext will be transferred to self-supervised learning, and in the process of self-supervised learning, the weights of other layers except the final linear layer are frozen without changing (these weights come from pretext).

Self-Supervised Learning Using Siamese Networks and Binary Classifier

Dušan mihajlov · march 2020.

In this thesis, we present several approaches for training a convolutional neural network using only unlabeled data. Our autonomously supervised learning algorithms are based on connections between image patch i. e. zoomed image and its original. Using the siamese architecture neural network we aim to recognize, if the image patch, which is input to the first neural network part, comes from the same image presented to the second neural network part. By applying transformations to both images, and different zoom sizes at different positions, we force the network to extract high level features using its convolutional layers. At the top of our siamese architecture, we have a simple binary classifier that measures the difference between feature maps that we extract and makes a decision. Thus, the only way that the classifier will solve the task correctly is when our convolutional layers are extracting useful representations. Those representations we can than use to solve many different tasks that are related to the data used for unsupervised training. As the main benchmark for all of our models, we used STL10 dataset, where we train a linear classifier on the top of our convolutional layers with a small amount of manually labeled images, which is a widely used benchmark for unsupervised learning tasks. We also combine our idea with recent work on the same topic, and the network called RotNet, which makes use of image rotations and therefore forces the network to learn rotation dependent features from the dataset. As a result of this combination we create a new procedure that outperforms original RotNet.

Learning Object Representations by Mixing Scenes

Lukas zbinden · may 2019.

In the digital age of ever increasing data amassment and accessibility, the demand for scalable machine learning models effective at refining the new oil is unprecedented. Unsupervised representation learning methods present a promising approach to exploit this invaluable yet unlabeled digital resource at scale. However, a majority of these approaches focuses on synthetic or simplified datasets of images. What if a method could learn directly from natural Internet-scale image data? In this thesis, we propose a novel approach for unsupervised learning of object representations by mixing natural image scenes. Without any human help, our method mixes visually similar images to synthesize new realistic scenes using adversarial training. In this process the model learns to represent and understand the objects prevalent in natural image data and makes them available for downstream applications. For example, it enables the transfer of objects from one scene to another. Through qualitative experiments on complex image data we show the effectiveness of our method along with its limitations. Moreover, we benchmark our approach quantitatively against state-of-the-art works on the STL-10 dataset. Our proposed method demonstrates the potential that lies in learning representations directly from natural image data and reinforces it as a promising avenue for future research.

Representation Learning using Semantic Distances

Markus roth · may 2019, zero-shot learning using generative adversarial networks, hamed hemati · dec. 2018, dimensionality reduction via cnns - learning the distance between images, ioannis glampedakis · sept. 2018, learning to play othello using deep reinforcement learning and self play, thomas simon steinmann · sept. 2018, aba-j interactive multi-modality tissue sectionto-volume alignment: a brain atlasing toolkit for imagej, felix meyenhofer · march 2018, learning visual odometry with recurrent neural networks, adrian wälchli · feb. 2018.

In computer vision, Visual Odometry is the problem of recovering the camera motion from a video. It is related to Structure from Motion, the problem of reconstructing the 3D geometry from a collection of images. Decades of research in these areas have brought successful algorithms that are used in applications like autonomous navigation, motion capture, augmented reality and others. Despite the success of these prior works in real-world environments, their robustness is highly dependent on manual calibration and the magnitude of noise present in the images in form of, e.g., non-Lambertian surfaces, dynamic motion and other forms of ambiguity. This thesis explores an alternative approach to the Visual Odometry problem via Deep Learning, that is, a specific form of machine learning with artificial neural networks. It describes and focuses on the implementation of a recent work that proposes the use of Recurrent Neural Networks to learn dependencies over time due to the sequential nature of the input. Together with a convolutional neural network that extracts motion features from the input stream, the recurrent part accumulates knowledge from the past to make camera pose estimations at each point in time. An analysis on the performance of this system is carried out on real and synthetic data. The evaluation covers several ways of training the network as well as the impact and limitations of the recurrent connection for Visual Odometry.

Crime location and timing prediction

Bernard swart · jan. 2018, from cartoons to real images: an approach to unsupervised visual representation learning, simon jenni · feb. 2017, automatic and large-scale assessment of fluid in retinal oct volume, nina mujkanovic · dec. 2016, segmentation in 3d using eye-tracking technology, michele wyss · july 2016, accurate scale thresholding via logarithmic total variation prior, remo diethelm · aug. 2014, novel techniques for robust and generalizable machine learning, abdelhak lemkhenter · sept. 2023.

Neural networks have transcended their status of powerful proof-of-concept machine learning into the realm of a highly disruptive technology that has revolutionized many quantitative fields such as drug discovery, autonomous vehicles, and machine translation. Today, it is nearly impossible to go a single day without interacting with a neural network-powered application. From search engines to on-device photo-processing, neural networks have become the go-to solution thanks to recent advances in computational hardware and an unprecedented scale of training data. Larger and less curated datasets, typically obtained through web crawling, have greatly propelled the capabilities of neural networks forward. However, this increase in scale amplifies certain challenges associated with training such models. Beyond toy or carefully curated datasets, data in the wild is plagued with biases, imbalances, and various noisy components. Given the larger size of modern neural networks, such models run the risk of learning spurious correlations that fail to generalize beyond their training data. This thesis addresses the problem of training more robust and generalizable machine learning models across a wide range of learning paradigms for medical time series and computer vision tasks. The former is a typical example of a low signal-to-noise ratio data modality with a high degree of variability between subjects and datasets. There, we tailor the training scheme to focus on robust patterns that generalize to new subjects and ignore the noisier and subject-specific patterns. To achieve this, we first introduce a physiologically inspired unsupervised training task and then extend it by explicitly optimizing for cross-dataset generalization using meta-learning. In the context of image classification, we address the challenge of training semi-supervised models under class imbalance by designing a novel label refinement strategy with higher local sensitivity to minority class samples while preserving the global data distribution. Lastly, we introduce a new Generative Adversarial Networks training loss. Such generative models could be applied to improve the training of subsequent models in the low data regime by augmenting the dataset using generated samples. Unfortunately, GAN training relies on a delicate balance between its components, making it prone mode collapse. Our contribution consists of defining a more principled GAN loss whose gradients incentivize the generator model to seek out missing modes in its distribution. All in all, this thesis tackles the challenge of training more robust machine learning models that can generalize beyond their training data. This necessitates the development of methods specifically tailored to handle the diverse biases and spurious correlations inherent in the data. It is important to note that achieving greater generalizability in models goes beyond simply increasing the volume of data; it requires meticulous consideration of training objectives and model architecture. By tackling these challenges, this research contributes to advancing the field of machine learning and underscores the significance of thoughtful design in obtaining more resilient and versatile models.

Automated Sleep Scoring, Deep Learning and Physician Supervision

Luigi fiorillo · oct. 2022.

Sleep plays a crucial role in human well-being. Polysomnography is used in sleep medicine as a diagnostic tool, so as to objectively analyze the quality of sleep. Sleep scoring is the procedure of extracting sleep cycle information from the wholenight electrophysiological signals. The scoring is done worldwide by the sleep physicians according to the official American Academy of Sleep Medicine (AASM) scoring manual. In the last decades, a wide variety of deep learning based algorithms have been proposed to automatise the sleep scoring task. In this thesis we study the reasons why these algorithms fail to be introduced in the daily clinical routine, with the perspective of bridging the existing gap between the automatic sleep scoring models and the sleep physicians. In this light, the primary step is the design of a simplified sleep scoring architecture, also providing an estimate of the model uncertainty. Beside achieving results on par with most up-to-date scoring systems, we demonstrate the efficiency of ensemble learning based algorithms, together with label smoothing techniques, in both enhancing the performance and calibrating the simplified scoring model. We introduced an uncertainty estimate procedure, so as to identify the most challenging sleep stage predictions, and to quantify the disagreement between the predictions given by the model and the annotation given by the physicians. In this thesis we also propose a novel method to integrate the inter-scorer variability into the training procedure of a sleep scoring model. We clearly show that a deep learning model is able to encode this variability, so as to better adapt to the consensus of a group of scorers-physicians. We finally address the generalization ability of a deep learning based sleep scoring system, further studying its resilience to the sleep complexity and to the AASM scoring rules. We can state that there is no need to train the algorithm strictly following the AASM guidelines. Most importantly, using data from multiple data centers results in a better performing model compared with training on a single data cohort. The variability among different scorers and data centers needs to be taken into account, more than the variability among sleep disorders.

Learning Representations for Controllable Image Restoration

Givi meishvili · march 2022.

Deep Convolutional Neural Networks have sparked a renaissance in all the sub-fields of computer vision. Tremendous progress has been made in the area of image restoration. The research community has pushed the boundaries of image deblurring, super-resolution, and denoising. However, given a distorted image, most existing methods typically produce a single restored output. The tasks mentioned above are inherently ill-posed, leading to an infinite number of plausible solutions. This thesis focuses on designing image restoration techniques capable of producing multiple restored results and granting users more control over the restoration process. Towards this goal, we demonstrate how one could leverage the power of unsupervised representation learning. Image restoration is vital when applied to distorted images of human faces due to their social significance. Generative Adversarial Networks enable an unprecedented level of generated facial details combined with smooth latent space. We leverage the power of GANs towards the goal of learning controllable neural face representations. We demonstrate how to learn an inverse mapping from image space to these latent representations, tuning these representations towards a specific task, and finally manipulating latent codes in these spaces. For example, we show how GANs and their inverse mappings enable the restoration and editing of faces in the context of extreme face super-resolution and the generation of novel view sharp videos from a single motion-blurred image of a face. This thesis also addresses more general blind super-resolution, denoising, and scratch removal problems, where blur kernels and noise levels are unknown. We resort to contrastive representation learning and first learn the latent space of degradations. We demonstrate that the learned representation allows inference of ground-truth degradation parameters and can guide the restoration process. Moreover, it enables control over the amount of deblurring and denoising in the restoration via manipulation of latent degradation features.

Learning Generalizable Visual Patterns Without Human Supervision

Simon jenni · oct. 2021.

Owing to the existence of large labeled datasets, Deep Convolutional Neural Networks have ushered in a renaissance in computer vision. However, almost all of the visual data we generate daily - several human lives worth of it - remains unlabeled and thus out of reach of today’s dominant supervised learning paradigm. This thesis focuses on techniques that steer deep models towards learning generalizable visual patterns without human supervision. Our primary tool in this endeavor is the design of Self-Supervised Learning tasks, i.e., pretext-tasks for which labels do not involve human labor. Besides enabling the learning from large amounts of unlabeled data, we demonstrate how self-supervision can capture relevant patterns that supervised learning largely misses. For example, we design learning tasks that learn deep representations capturing shape from images, motion from video, and 3D pose features from multi-view data. Notably, these tasks’ design follows a common principle: The recognition of data transformations. The strong performance of the learned representations on downstream vision tasks such as classiﬁcation, segmentation, action recognition, or pose estimation validate this pretext-task design. This thesis also explores the use of Generative Adversarial Networks (GANs) for unsupervised representation learning. Besides leveraging generative adversarial learning to deﬁne image transformation for self-supervised learning tasks, we also address training instabilities of GANs through the use of noise. While unsupervised techniques can signiﬁcantly reduce the burden of supervision, in the end, we still rely on some annotated examples to ﬁne-tune learned representations towards a target task. To improve the learning from scarce or noisy labels, we describe a supervised learning algorithm with improved generalization in these challenging settings.

Learning Interpretable Representations of Images

Attila szabó · june 2019.

Computers represent images with pixels and each pixel contains three numbers for red, green and blue colour values. These numbers are meaningless for humans and they are mostly useless when used directly with classical machine learning techniques like linear classifiers. Interpretable representations are the attributes that humans understand: the colour of the hair, viewpoint of a car or the 3D shape of the object in the scene. Many computer vision tasks can be viewed as learning interpretable representations, for example a supervised classification algorithm directly learns to represent images with their class labels. In this work we aim to learn interpretable representations (or features) indirectly with lower levels of supervision. This approach has the advantage of cost savings on dataset annotations and the flexibility of using the features for multiple follow-up tasks. We made contributions in three main areas: weakly supervised learning, unsupervised learning and 3D reconstruction. In the weakly supervised case we use image pairs as supervision. Each pair shares a common attribute and differs in a varying attribute. We propose a training method that learns to separate the attributes into separate feature vectors. These features then are used for attribute transfer and classification. We also show theoretical results on the ambiguities of the learning task and the ways to avoid degenerate solutions. We show a method for unsupervised representation learning, that separates semantically meaningful concepts. We explain and show ablation studies how the components of our proposed method work: a mixing autoencoder, a generative adversarial net and a classifier. We propose a method for learning single image 3D reconstruction. It is done using only the images, no human annotation, stereo, synthetic renderings or ground truth depth map is needed. We train a generative model that learns the 3D shape distribution and an encoder to reconstruct the 3D shape. For that we exploit the notion of image realism. It means that the 3D reconstruction of the object has to look realistic when it is rendered from different random angles. We prove the efficacy of our method from first principles.

Learning Controllable Representations for Image Synthesis

Qiyang hu · june 2019.

In this thesis, our focus is learning a controllable representation and applying the learned controllable feature representation on images synthesis, video generation, and even 3D reconstruction. We propose different methods to disentangle the feature representation in neural network and analyze the challenges in disentanglement such as reference ambiguity and shortcut problem when using the weak label. We use the disentangled feature representation to transfer attributes between images such as exchanging hairstyle between two face images. Furthermore, we study the problem of how another type of feature, sketch, works in a neural network. The sketch can provide shape and contour of an object such as the silhouette of the side-view face. We leverage the silhouette constraint to improve the 3D face reconstruction from 2D images. The sketch can also provide the moving directions of one object, thus we investigate how one can manipulate the object to follow the trajectory provided by a user sketch. We propose a method to automatically generate video clips from a single image input using the sketch as motion and trajectory guidance to animate the object in that image. We demonstrate the efficiency of our approaches on several synthetic and real datasets.

Beyond Supervised Representation Learning

Mehdi noroozi · jan. 2019.

The complexity of any information processing task is highly dependent on the space where data is represented. Unfortunately, pixel space is not appropriate for the computer vision tasks such as object classification. The traditional computer vision approaches involve a multi-stage pipeline where at first images are transformed to a feature space through a handcrafted function and then consequenced by the solution in the feature space. The challenge with this approach is the complexity of designing handcrafted functions that extract robust features. The deep learning based approaches address this issue by end-to-end training of a neural network for some tasks that lets the network to discover the appropriate representation for the training tasks automatically. It turns out that image classification task on large scale annotated datasets yields a representation transferable to other computer vision tasks. However, supervised representation learning is limited to annotations. In this thesis we study self-supervised representation learning where the goal is to alleviate these limitations by substituting the classification task with pseudo tasks where the labels come for free. We discuss self-supervised learning by solving jigsaw puzzles that uses context as supervisory signal. The rational behind this task is that the network requires to extract features about object parts and their spatial configurations to solve the jigsaw puzzles. We also discuss a method for representation learning that uses an artificial supervisory signal based on counting visual primitives. This supervisory signal is obtained from an equivariance relation. We use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image. The most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. We discuss a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific finetuned model. Finally, we study the problem of multi-task representation learning. A naive approach to enhance the representation learned by a task is to train the task jointly with other tasks that capture orthogonal attributes. Having a diverse set of auxiliary tasks, imposes challenges on multi-task training from scratch. We propose a framework that allows us to combine arbitrarily different feature spaces into a single deep neural network. We reduce the auxiliary tasks to classification tasks and the multi-task learning to multi-label classification task consequently. Nevertheless, combining multiple representation space without being aware of the target task might be suboptimal. As our second contribution, we show empirically that this is indeed the case and propose to combine multiple tasks after the fine-tuning on the target task.

Motion Deblurring from a Single Image

Meiguang jin · dec. 2018.

With the information explosion, a tremendous amount photos is captured and shared via social media everyday. Technically, a photo requires a finite exposure to accumulate light from the scene. Thus, objects moving during the exposure generate motion blur in a photo. Motion blur is an image degradation that makes visual content less interpretable and is therefore often seen as a nuisance. Although motion blur can be reduced by setting a short exposure time, an insufficient amount of light has to be compensated through increasing the sensor’s sensitivity, which will inevitably bring large amount of sensor noise. Thus this motivates the necessity of removing motion blur computationally. Motion deblurring is an important problem in computer vision and it is challenging due to its ill-posed nature, which means the solution is not well defined. Mathematically, a blurry image caused by uniform motion is formed by the convolution operation between a blur kernel and a latent sharp image. Potentially there are infinite pairs of blur kernel and latent sharp image that can result in the same blurry image. Hence, some prior knowledge or regularization is required to address this problem. Even if the blur kernel is known, restoring the latent sharp image is still difficult as the high frequency information has been removed. Although we can model the uniform motion deblurring problem mathematically, it can only address the camera in-plane translational motion. Practically, motion is more complicated and can be non-uniform. Non-uniform motion blur can come from many sources, camera out-of-plane rotation, scene depth change, object motion and so on. Thus, it is more challenging to remove non-uniform motion blur. In this thesis, our focus is motion blur removal. We aim to address four challenging motion deblurring problems. We start from the noise blind image deblurring scenario where blur kernel is known but the noise level is unknown. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0−1 loss to address this problem. Then we study the blind uniform motion deblurring scenario where both the blur kernel and the latent sharp image are unknown. We explore the relative scale ambiguity between the latent sharp image and blur kernel to address this issue. Moreover, we study the face deblurring problem and introduce a novel deep learning network architecture to solve it. We also address the general motion deblurring problem and particularly we aim at recovering a sequence of 7 frames each depicting some instantaneous motion of the objects in the scene.

Towards a Novel Paradigm in Blind Deconvolution: From Natural to Cartooned Image Statistics

Daniele perrone · july 2015.

In this thesis we study the blind deconvolution problem. Blind deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Recently it has been shown that a class of blind deconvolution formulations and image priors has the no-blur solution as global minimum. Despite this shortcoming, algorithms based on these formulations and priors can successfully solve blind deconvolution. In this thesis we show that a suitable initialization can exploit the non-convexity of the problem and yield the desired solution. Based on these conclusions, we propose a novel “vanilla” algorithm stripped of any enhancement typically used in the literature. Our algorithm, despite its simplicity, is able to compete with the top performers on several datasets. We have also investigated a remarkable behavior of a 1998 algorithm, whose formulation has the no-blur solution as global minimum: even when initialized at the no-blur solution, it converges to the correct solution. We show that this behavior is caused by an apparently insignificant implementation strategy that makes the algorithm no longer minimize the original cost functional. We also demonstrate that this strategy improves the results of our “vanilla” algorithm. Finally, we present a study of image priors for blind deconvolution. We provide experimental evidence supporting the recent belief that a good image prior is one that leads to a good blur estimate rather than being a good natural image statistical model. By focusing the attention on the blur estimation alone, we show that good blur estimates can be obtained even when using images quite different from the true sharp image. This allows using image priors, such as those leading to “cartooned” images, that avoid the no-blur solution. By using an image prior that produces “cartooned” images we achieve state-of-the-art results on different publicly available datasets. We therefore suggests a shift of paradigm in blind deconvolution: from modeling natural image statistics to modeling cartooned image statistics.

New Perspectives on Uncalibrated Photometric Stereo

Thoma papadhimitri · june 2014.

This thesis investigates the problem of 3D reconstruction of a scene from 2D images. In particular, we focus on photometric stereo which is a technique that computes the 3D geometry from at least three images taken from the same viewpoint and under different illumination conditions. When the illumination is unknown (uncalibrated photometric stereo) the problem is ambiguous: different combinations of geometry and illumination can generate the same images. First, we solve the ambiguity by exploiting the Lambertian reflectance maxima. These are points defined on curved surfaces where the normals are parallel to the light direction. Then, we propose a solution that can be computed in closed-form and thus very efficiently. Our algorithm is also very robust and yields always the same estimate regardless of the initial ambiguity. We validate our method on real world experiments and achieve state-of-art results. In this thesis we also solve for the first time the uncalibrated photometric stereo problem under the perspective projection model. We show that unlike in the orthographic case, one can uniquely reconstruct the normals of the object and the lights given only the input images and the camera calibration (focal length and image center). We also propose a very efficient algorithm which we validate on synthetic and real world experiments and show that the proposed technique is a generalization of the orthographic case. Finally, we investigate the uncalibrated photometric stereo problem in the case where the lights are distributed near the scene. In this case we propose an alternating minimization technique which converges quickly and overcomes the limitations of prior work that assumes distant illumination. We show experimentally that adopting a near-light model for real world scenes yields very accurate reconstructions.

Top 100+ Computer Engineering Project Topics [Updated]

Computer engineering projects offer a captivating blend of creativity and technical prowess, allowing enthusiasts to dive into a world where innovation meets functionality. Whether you’re fascinated by hardware design, software development, networking, or artificial intelligence, there’s a wide array of project topics to explore within the realm of computer engineering. In this blog, we’ll delve into some intriguing computer engineering project topics, catering to both beginners and seasoned enthusiasts alike.

What Is A CSE Project?

Table of Contents

A CSE project refers to a project within the field of Computer Science and Engineering (CSE). These projects involve the application of computer science principles and engineering techniques to develop software, hardware, or systems that solve real-world problems or advance technology.

CSE projects can range from developing new algorithms and programming languages to designing and building computer hardware, networking systems, software applications, or artificial intelligence systems.

They often require interdisciplinary knowledge and skills in areas such as programming, data structures, algorithms, software engineering, hardware design, networking, and more.

How Do I Start A CSE Project?

Starting a CSE (Computer Science and Engineering) project can be an exciting endeavor, but it requires careful planning and preparation. Here’s a step-by-step guide to help you get started:

Define Your Project Scope and Goals:
Identify the problem or opportunity you want to address with your project.
Clearly define the objectives and outcomes you aim to achieve.
Determine the scope of your project, including the technologies, tools, and resources you’ll need.
Conduct Research:
Research existing solutions and technologies related to your project idea.
Identify any gaps or opportunities for innovation in the field.
Explore relevant literature, academic papers, online resources, and case studies to gain insights and inspiration.
Choose a Project Topic:
Based on your research, select a specific topic or area of focus for your project.
Take into account your passions, abilities, and the assets at your disposal.
Make sure that the topic you select corresponds with the aims and objectives of your project.
Develop a Project Plan:
Make a thorough plan for your project by writing down all the things you need to do, when you need to do them, and what you want to achieve at different points.
Break the project into smaller parts that are easier to handle, and if you’re working with others, make sure everyone knows what they’re responsible for.
Define the deliverables and criteria for success for each phase of the project.
Gather Resources:
Identify the software, hardware, and other resources you’ll need for your project.
Set up development environments, programming tools, and any necessary infrastructure.
Consider collaborating with peers, mentors, or experts who can provide guidance and support.
Design Your Solution:
Develop a conceptual design or architecture for your project.
Define the system requirements, data structures, algorithms, and user interfaces.
Consider usability, scalability, security, and other factors in your design decisions.
Implement Your Project:
Start building your project based on the design and specifications you’ve developed.
Write code, design user interfaces, implement algorithms, and integrate components as needed.
Test your project continuously throughout the development process to identify and fix any issues early on.
Iterate and Refine:
Iterate on your project based on feedback and testing results.
Refine your implementation, make improvements, and address any issues or challenges that arise.
Continuously evaluate your progress against your project plan and adjust as necessary.
Document Your Work:
Keep detailed documentation of your project, including design decisions, code comments, and user manuals.
Document any challenges you faced, solutions you implemented, and lessons learned throughout the project.
Present Your Project:
Prepare a presentation or demo showcasing your project’s features, functionality, and achievements.
Communicate your project’s goals, methodology, results, and impact effectively to your audience.
Solicit feedback from peers, instructors, or industry professionals to gain insights and improve your project.

By following these steps and staying organized, focused, and adaptable, you can successfully start and complete a CSE project that not only enhances your skills and knowledge but also makes a meaningful contribution to the field of computer science and engineering.

Top 100+ Computer Engineering Project Topics

Design and Implementation of a Simple CPU
Development of a Real-time Operating System Kernel
Construction of a Digital Signal Processor (DSP)
Designing an FPGA-based Video Processing System
Building a GPU for Parallel Computing
Development of a Low-Power Microcontroller System
Designing an Efficient Cache Memory Architecture
Construction of a Network-on-Chip (NoC) for Multicore Systems
Development of a Hardware-based Encryption Engine
Designing a Reconfigurable Computing Platform
Building a RISC-V Processor Core
Development of a Custom Instruction Set Architecture (ISA)
Designing an Energy-Efficient Embedded System
Construction of a High-Speed Serial Communication Interface
Developing a Real-time Embedded System for Robotics
Designing an IoT-based Home Automation System
Building a Wearable Health Monitoring Device
Development of a Wireless Sensor Network for Environmental Monitoring
Designing an Automotive Control System
Building a GPS Tracking System for Vehicles
Development of a Smart Grid Monitoring System
Designing a Digital Audio Processor for Music Synthesis
Building a Speech Recognition System
Developing a Biometric Authentication System
Designing a Facial Recognition Security System
Construction of an Autonomous Drone
Development of a Gesture Recognition Interface
Designing an Augmented Reality Application
Building a Virtual Reality Simulator
Developing a Haptic Feedback System
Designing a Real-time Video Streaming Platform
Building a Multimedia Content Delivery Network (CDN)
Development of a Scalable Web Server Architecture
Designing a Peer-to-Peer File Sharing System
Building a Distributed Database Management System
Developing a Blockchain-based Voting System
Designing a Secure Cryptocurrency Exchange Platform
Building an Anonymous Communication Network
Development of a Secure Email Encryption System
Designing a Network Intrusion Detection System (NIDS)
Building a Firewall with Deep Packet Inspection (DPI)
Developing a Vulnerability Assessment Tool
Designing a Secure Password Manager Application
Building a Malware Analysis Sandbox
Development of a Phishing Detection System
Designing a Chatbot for Customer Support
Building a Natural Language Processing (NLP) System
Developing an AI-powered Personal Assistant
Designing a Recommendation System for E-commerce
Building an Intelligent Tutoring System
Development of a Sentiment Analysis Tool
Designing an Autonomous Vehicle Navigation System
Building a Traffic Management System
Developing a Smart Parking Solution
Designing a Remote Health Monitoring System
Building a Telemedicine Platform
Development of a Medical Image Processing Application
Designing a Drug Discovery System
Building a Healthcare Data Analytics Platform
Developing a Smart Agriculture Solution
Designing a Crop Monitoring System
Building an Automated Irrigation System
Developing a Food Quality Inspection Tool
Designing a Supply Chain Management System
Building a Warehouse Automation Solution
Developing a Inventory Optimization Tool
Designing a Smart Retail Store System
Building a Self-checkout System
Developing a Customer Behavior Analytics Platform
Designing a Fraud Detection System for Banking
Building a Risk Management Solution
Developing a Personal Finance Management Application
Designing a Stock Market Prediction System
Building a Portfolio Management Tool
Developing a Smart Energy Management System
Designing a Home Energy Monitoring Solution
Building a Renewable Energy Integration Platform
Developing a Smart Grid Demand Response System
Designing a Disaster Management System
Building an Emergency Response Coordination Tool
Developing a Weather Prediction and Monitoring System
Designing a Climate Change Mitigation Solution
Building a Pollution Monitoring and Control System
Developing a Waste Management Optimization Tool
Designing a Smart City Infrastructure Management System
Building a Traffic Congestion Management Solution
Developing a Public Safety and Security Platform
Designing a Citizen Engagement and Participation System
Building a Smart Transportation Network
Developing a Smart Water Management System
Designing a Water Quality Monitoring and Control System
Building a Flood Detection and Response System
Developing a Coastal Erosion Prediction Tool
Designing an Air Quality Monitoring and Control System
Building a Green Building Energy Optimization Solution
Developing a Sustainable Transportation Planning Tool
Designing a Wildlife Conservation Monitoring System
Building a Biodiversity Mapping and Protection Platform
Developing a Natural Disaster Early Warning System
Designing a Remote Sensing and GIS Integration Solution
Building a Climate Change Adaptation and Resilience Platform

7 Helpful Tips for Final Year Engineering Project

Embarking on a final year engineering project can be both exhilarating and daunting. Here are seven helpful tips to guide you through the process and ensure the success of your project:

Start Early and Plan Thoroughly

Begin planning your project as soon as possible to allow ample time for research, design, and implementation.
Break down your project into smaller tasks and create a detailed timeline with milestones to track your progress.
Consider any potential challenges or obstacles you may encounter and plan contingencies accordingly.

Choose the Right Project

Select a project that aligns with your interests, skills, and career goals.
Ensure that the project is feasible within the time and resource constraints of your final year.
Seek advice from professors, mentors, or industry professionals to help you choose a project that is both challenging and achievable.

Conduct Thorough Research

Invest time in researching existing solutions, technologies, and literature related to your project idea.
Identify gaps or opportunities for innovation that your project can address.
Keep track of relevant papers, articles, and resources to inform your design and implementation decisions.

Communicate Effectively

Maintain regular communication with your project advisor or supervisor to seek guidance and feedback.
Collaborate effectively with teammates, if applicable, by establishing clear channels of communication and dividing tasks appropriately.
Practice effective communication skills when presenting your project to classmates, professors, or industry professionals.

Focus on Quality and Innovation

Strive for excellence in every aspect of your project, from design and implementation to documentation and presentation.
Try to come up with new ideas and find ways to make them better than what’s already out there.
Make sure you do your work carefully and make it the best it can be.

Test and Iterate

Test your project rigorously throughout the development process to identify and address any issues or bugs.
Solicit feedback from peers, advisors, or end-users to gain insights and improve your project.
Iterate on your design and implementation based on feedback and testing results to refine your solution and enhance its functionality.

Manage Your Time Effectively

Prioritize tasks and allocate time wisely to ensure that you meet deadlines and deliverables.
Break down larger tasks into smaller, manageable chunks and tackle them one at a time.
Stay organized with tools such as calendars, to-do lists, and project management software to track your progress and stay on schedule.

By following these tips and staying focused, disciplined, and proactive, you can navigate the challenges of your final year engineering project with confidence and achieve outstanding results. Remember to stay flexible and adaptable, and don’t hesitate to seek help or advice when needed. Good luck!

Computer engineering project topics offer a unique opportunity to blend creativity with technical expertise, empowering enthusiasts to explore diverse domains of computing while tackling real-world challenges. Whether you’re interested in hardware design, software development, networking, or artificial intelligence, there’s a wealth of project topics to inspire innovation and learning.

By starting these projects, people who are passionate about it can improve their abilities, learn more, and add to the changing world of technology. So, get ready to work hard, let your imagination flow, and begin an exciting adventure of learning and discovery in the amazing field of computer engineering.

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Home > Engineering > ECE > Electrical & Computer Engineering Masters Theses Collection

Electrical & Computer Engineering Masters Theses Collection

Theses from 2024 2024.

Extracting DNN Architectures Via Runtime Profiling On Mobile GPUs , Dong Hyub Kim, Electrical & Computer Engineering

Semantic-Aware Blockchain Architecture Design for Lifelong Edge-enabled Metaverse , Ning Wang, Electrical & Computer Engineering

Blockchain Design for a Secure Pharmaceutical Supply Chain , Zhe Xu, Electrical & Computer Engineering

Collaborative Caching and Computation Offloading for Intelligent Transportation Systems enabled by Satellite-Airborne-Terrestrial Networks , Shulun Yang, Electrical & Computer Engineering

Protecting Return Address Integrity for RISC-V via Pointer Authentication , yuhe zhao, Electrical & Computer Engineering

Theses from 2023 2023

Fingerprinting for Chiplet Architectures Using Power Distribution Network Transients , Matthew G. Burke, Electrical & Computer Engineering

Design and Fabrication of a Trapped Ion Quantum Computing Testbed , Christopher A. Caron, Electrical & Computer Engineering

Analog Cancellation of a Known Remote Interference: Hardware Realization and Analysis , James M. Doty, Electrical & Computer Engineering

Electrothermal Properties of 2D Materials in Device Applications , Samantha L. Klein, Electrical & Computer Engineering

Ablation Study on Deeplabv3+ for Semantic Segmentation , Bowen Lei, Electrical & Computer Engineering

A Composability-Based Transformer Pruning Framework , Yuping Lin, Electrical & Computer Engineering

A Model Extraction Attack on Deep Neural Networks Running on GPUs , Jonah G. O'Brien Weiss, Electrical & Computer Engineering

Heterogeneous IoT Network Architecture Design for Age of Information Minimization , Xiaohao Xia, Electrical & Computer Engineering

Theses from 2022 2022

Theory and Analysis of Backprojection Processing for Interferometric SAR , Marc Closa Tarres, Electrical & Computer Engineering

Unpaired Skeleton-to-Photo Translation for Sketch-to-Photo Synthesis , Yuanzhe Gu, Electrical & Computer Engineering

Integration of Digital Signal Processing Block in SymbiFlow FPGA Toolchain for Artix-7 Devices , Andrew T. Hartnett, Electrical & Computer Engineering

Planar Ultra-Wideband Modular Antenna (PUMA) Arrays for High-Volume Manufacturing on Organic Laminates and BGA Interfaces , James R. LaCroix, Electrical & Computer Engineering

Planar Transmission-Line Metamaterials on an Irregular Grid , Tina E. Maurer, Electrical & Computer Engineering

Formally Verifiable Synthesis Flow In FPGAs , Anurag V. Muttur, Electrical & Computer Engineering

Theses from 2021 2021

Graph-Algorithm Based Verification on Network Configuration Robustness , Zibin Chen, Electrical & Computer Engineering

A Cloud Infrastructure for Large Scale Health Monitoring in Older Adult Care Facilities , Uchechukwu Gabriel David, Electrical & Computer Engineering

Internet Infrastructures for Large Scale Emulation with Efficient HW/SW Co-design , Aiden K. Gula, Electrical & Computer Engineering

Mtemp: An Ambient Temperature Estimation Method Using Acoustic Signal on Mobile Devices , Hao Guo, Electrical & Computer Engineering

BENCHMARKING SMALL-DATASET STRUCTURE-ACTIVITY-RELATIONSHIP MODELS FOR PREDICTION OF WNT SIGNALING INHIBITION , Mahtab Kokabi, Electrical & Computer Engineering

ACTION : Adaptive Cache Block Migration in Distributed Cache Architectures , Chandra Sekhar Mummidi, Electrical & Computer Engineering

Modeling and Characterization of Optical Metasurfaces , Mahsa Torfeh, Electrical & Computer Engineering

TickNet: A Lightweight Deep Classifier for Tick Recognition , Li Wang, Electrical & Computer Engineering

Lecture Video Transformation through An Intelligent Analysis and Post-processing System , Xi Wang, Electrical & Computer Engineering

Correcting For Terrain Interference, Attenuation, and System Bias for a Dual Polarimetric, X-Band Radar , Casey Wolsieffer, Electrical & Computer Engineering

Theses from 2020 2020

Numerical Simulation of Thermoelectric Transport in Bulk and Nanostructured SiSn Alloys , Venkatakrishna Dusetty, Electrical & Computer Engineering

Deep Reinforcement Learning For Distributed Fog Network Probing , Xiaoding Guan, Electrical & Computer Engineering

COMPRESSIVE PARAMETER ESTIMATION VIA APPROXIMATE MESSAGE PASSING , Shermin Hamzehei, Electrical & Computer Engineering

Metric Learning via Linear Embeddings for Human Motion Recognition , ByoungDoo Kong, Electrical & Computer Engineering

Compound Effects of Clock and Voltage Based Power Side-Channel Countermeasures , Jacqueline Lagasse, Electrical & Computer Engineering

Network Virtualization and Emulation using Docker, OpenvSwitch and Mininet-based Link Emulation , Narendra Prabhu, Electrical & Computer Engineering

Thermal Transport Modeling Of Semiconductor Materials From First Principles , Aliya Qureshi, Electrical & Computer Engineering

CROSSTALK BASED SIDE CHANNEL ATTACKS IN FPGAs , Chethan Ramesh, Electrical & Computer Engineering

Accelerating RSA Public Key Cryptography via Hardware Acceleration , Pavithra Ramesh, Electrical & Computer Engineering

Real-Time TDDFT-Based Filtered Spectroscopy , Ivan Williams, Electrical & Computer Engineering

Perception System: Object and Landmark Detection for Visually Impaired Users , Chenguang Zhang, Electrical & Computer Engineering

Theses from 2019 2019

An Empirical Analysis of Network Traffic: Device Profiling and Classification , Mythili Vishalini Anbazhagan, Electrical & Computer Engineering

Pre-Travel Training And Real-Time Guidance System For People With Disabilities In Indoor Environments , Binru Cao, Electrical & Computer Engineering

Energy Efficiency of Computation in All-spin Logic: Projections and Fundamental Limits , Zongya Chen, Electrical & Computer Engineering

Improving Resilience of Communication in Information Dissemination for Time-Critical Applications , Rajvardhan Somraj Deshmukh, Electrical & Computer Engineering

InSAR Simulations for SWOT and Dual Frequency Processing for Topographic Measurements , Gerard Masalias Huguet, Electrical & Computer Engineering

A Study on Controlling Power Supply Ramp-Up Time in SRAM PUFs , Harshavardhan Ramanna, Electrical & Computer Engineering

The UMass Experimental X-Band Radar (UMAXX): An Upgrade of the CASA MA-1 to Support Cross-Polarization Measurements , Jezabel Vilardell Sanchez, Electrical & Computer Engineering

A Video-Based System for Emergency Preparedness and Recovery , Juechen Yin, Electrical & Computer Engineering

Theses from 2018 2018

Phonon Transport at Boundaries and Interfaces in Two-Dimensional Materials , Cameron Foss, Electrical & Computer Engineering

SkinnySensor: Enabling Battery-Less Wearable Sensors Via Intrabody Power Transfer , Neev Kiran, Electrical & Computer Engineering

Immersive Pre-travel Training Application for Seniors and People with Disabilities , Yang Li, Electrical & Computer Engineering

Analog Computing using 1T1R Crossbar Arrays , Yunning Li, Electrical & Computer Engineering

On-Chip Communication and Security in FPGAs , Shivukumar Basanagouda Patil, Electrical & Computer Engineering

CROWDSOURCING BASED MICRO NAVIGATION SYSTEM FOR VISUALLY IMPAIRED , Quan Shi, Electrical & Computer Engineering

AN EVALUATION OF SDN AND NFV SUPPORT FOR PARALLEL, ALTERNATIVE PROTOCOL STACK OPERATIONS IN FUTURE INTERNETS , Bhushan Suresh, Electrical & Computer Engineering

Applications Of Physical Unclonable Functions on ASICS and FPGAs , Mohammad Usmani, Electrical & Computer Engineering

Improvements to the UMASS S-Band FM-CW Vertical Wind Profiling Radar: System Performance and Data Analysis. , Joseph Waldinger, Electrical & Computer Engineering

Theses from 2017 2017

AutoPlug: An Automated Metadata Service for Smart Outlets , Lurdh Pradeep Reddy Ambati, Electrical & Computer Engineering

SkyNet: Memristor-based 3D IC for Artificial Neural Networks , Sachin Bhat, Electrical & Computer Engineering

Navigation Instruction Validation Tool and Indoor Wayfinding Training System for People with Disabilities , Linlin Ding, Electrical & Computer Engineering

Energy Efficient Loop Unrolling for Low-Cost FPGAs , Naveen Kumar Dumpala, Electrical & Computer Engineering

Effective Denial of Service Attack on Congestion Aware Adaptive Network on Chip , Vijaya Deepak Kadirvel, Electrical & Computer Engineering

VIRTUALIZATION OF CLOSED-LOOP SENSOR NETWORKS , Priyanka Dattatri Kedalagudde, Electrical & Computer Engineering

The Impact of Quantum Size Effects on Thermoelectric Performance in Semiconductor Nanostructures , Adithya Kommini, Electrical & Computer Engineering

MAGNETO-ELECTRIC APPROXIMATE COMPUTATIONAL FRAMEWORK FOR BAYESIAN INFERENCE , Sourabh Kulkarni, Electrical & Computer Engineering

Time Domain SAR Processing with GPUs for Airborne Platforms , Dustin Lagoy, Electrical & Computer Engineering

Query on Knowledge Graphs with Hierarchical Relationships , Kaihua Liu, Electrical & Computer Engineering

HIGH PERFORMANCE SILVER DIFFUSIVE MEMRISTORS FOR FUTURE COMPUTING , Rivu Midya, Electrical & Computer Engineering

Achieving Perfect Location Privacy in Wireless Devices Using Anonymization , Zarrin Montazeri, Electrical & Computer Engineering

KaSI: a Ka-band and S-band Cross-track Interferometer , Gerard Ruiz Carregal, Electrical & Computer Engineering

Analyzing Spark Performance on Spot Instances , Jiannan Tian, Electrical & Computer Engineering

Indoor Navigation For The Blind And Visually Impaired: Validation And Training Methodology Using Virtual Reality , Sili Wang, Electrical & Computer Engineering

Efficient Scaling of a Web Proxy Cluster , Hao Zhang, Electrical & Computer Engineering

ORACLE GUIDED INCREMENTAL SAT SOLVING TO REVERSE ENGINEER CAMOUFLAGED CIRCUITS , Xiangyu Zhang, Electrical & Computer Engineering

Theses from 2016 2016

Seamless Application Delivery Using Software Defined Exchanges , Divyashri Bhat, Electrical & Computer Engineering

PROCESSOR TEMPERATURE AND RELIABILITY ESTIMATION USING ACTIVITY COUNTERS , Mayank Chhablani, Electrical & Computer Engineering

PARQ: A MEMORY-EFFICIENT APPROACH FOR QUERY-LEVEL PARALLELISM , Qianqian Gao, Electrical & Computer Engineering

Accelerated Iterative Algorithms with Asynchronous Accumulative Updates on a Heterogeneous Cluster , Sandesh Gubbi Virupaksha, Electrical & Computer Engineering

Improving Efficiency of Thermoelectric Devices Made of Si-Ge, Si-Sn, Ge-Sn, and Si-Ge-Sn Binary and Ternary Alloys , Seyedeh Nazanin Khatami, Electrical & Computer Engineering

6:1 PUMA Arrays: Designs and Finite Array Effects , Michael Lee, Electrical & Computer Engineering

Protecting Controllers against Denial-of-Service Attacks in Software-Defined Networks , Jingrui Li, Electrical & Computer Engineering

INFRASTRUCTURE-FREE SECURE PAIRING OF MOBILE DEVICES , Chunqiu Liu, Electrical & Computer Engineering

Extrinsic Effects on Heat and Electron Transport In Two-Dimensional Van-Der Waals Materials- A Boltzmann Transport Study , Arnab K. Majee, Electrical & Computer Engineering

SpotLight: An Information Service for the Cloud , Xue Ouyang, Electrical & Computer Engineering

Localization, Visualization And Evacuation Guidance System In Emergency Situations , Jingyan Tang, Electrical & Computer Engineering

Variation Aware Placement for Efficient Key Generation using Physically Unclonable Functions in Reconfigurable Systems , Shrikant S. Vyas, Electrical & Computer Engineering

EVALUATING FEATURES FOR BROAD SPECIES BASED CLASSIFICATION OF BIRD OBSERVATIONS USING DUAL-POLARIZED DOPPLER WEATHER RADAR , Sheila Werth, Electrical & Computer Engineering

Theses from 2015 2015

Quality Factor of Horizontal Wire Dipole Antennas near Planar Conductor or Dielectric Interface , Adebayo Gabriel Adeyemi, Electrical & Computer Engineering

Evaluation of Two-Dimensional Codes for Digital Information Security in Physical Documents , Shuai Chen, Electrical & Computer Engineering

Design and Implementation of a High Performance Network Processor with Dynamic Workload Management , Padmaja Duggisetty, Electrical & Computer Engineering

Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification , Siwei Feng, Electrical & Computer Engineering

DEVELOPMENT OF INFRARED AND TERAHERTZ BOLOMETERS BASED ON PALLADIUM AND CARBON NANOTUBES USING ROLL TO ROLL PROCESS , Amulya Gullapalli, Electrical & Computer Engineering

Development of Prototypes of a Portable Road Weather Information System , Meha Kainth, Electrical & Computer Engineering

ADACORE: Achieving Energy Efficiency via Adaptive Core Morphing at Runtime , Nithesh Kurella, Electrical & Computer Engineering

Architecting SkyBridge-CMOS , Mingyu Li, Electrical & Computer Engineering

Function Verification of Combinational Arithmetic Circuits , Duo Liu, Electrical & Computer Engineering

ENERGY EFFICIENCY EXPLORATION OF COARSE-GRAIN RECONFIGURABLE ARCHITECTURE WITH EMERGING NONVOLATILE MEMORY , Xiaobin Liu, Electrical & Computer Engineering

Development of a Layout-Level Hardware Obfuscation Tool to Counter Reverse Engineering , Shweta Malik, Electrical & Computer Engineering

Energy Agile Cluster Communication , Muhammad Zain Mustafa, Electrical & Computer Engineering

Architecting NP-Dynamic Skybridge , Jiajun Shi, Electrical & Computer Engineering

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Bachelor and Master Thesis

We offer a variety of cutting-edge and exciting research topics for Bachelor's and Master's theses. We cover a wide range of topics from Data Science, Natural Language Processing, Argument Mining, the Use of AI in Business, Ethics in AI and Multimodal AI. We are always open to suggestions for your own topics, so please feel free to contact us. We supervise students from all disciplines of business administration, business informatics, computer science and industrial engineering.

Thesis Topics

Example topics could be:

Conversational Artificial Intelligence in Insurance and Finance
Natural Language Processing for Understanding Financial Narratives: An Overview
Ethics at the Intersection of Finance and AI: A Comprehensive Literature Review
Explainable Natural Language Processing for Credit Risk Assessment Models: A Literature Review

Thesis Template

Latex Template for bachelor and master theses
How to use the latex template

Q1: How many pages do I need to write?

A: In general, the number of pages is only a poor indicator of the quality of a thesis. However, as a rule of thumb, bachelor theses should have around 30 pages, while master theses should be around 60 pages of main content (that is, without the appendix and lists of tables, symbols, figures, references etc.).

Q2: How often should I meet with my supervisor?

A: Your supervisors are typically very busy people. However, don't hesitate to ask in case you have questions. For instance, if you are unsure of some requirements, or in case you have methodological problems, it is absolutely necessary to talk to your supervisor. As a rule of thumb, you should meet at least three times (once in the beginning, once in the middle, and once before the submission).

Q3: Am I allowed to use any AI models in the process of writing my thesis?

A: In general, we neither forbid nor recommend the use of AI for writing support. However, if you use AI, please inform your supervisor. Also, you need to adhere to the recommendations on the use of AI writing assistants given by the faculty.

Q4: How much time do I have?

A: The exact timing is dependent on your study program! Thus, please check the examination requirements before the official start of your thesis -- you are responsible for sticking to the rules.

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1000 Computer Science Thesis Topics and Ideas
This section offers a well-organized and extensive list of 1000 computer science thesis topics, designed to illuminate diverse pathways for academic inquiry and innovation. Whether your interest lies in the emerging trends of artificial intelligence or the practical applications of web development, this assortment spans 25 critical areas of ...
Top 101 Computer Science Research Topics
This is a set of 100 original and interesting research paper topics on computer science that is free to download and use for any academic assignment. Toll-free: +1 (877) 401-4335. Order Now. About; Prices; ... Computer Science Thesis Topics for College Students. How can logic and sets be used in computing?
Computer Science Graduate Projects and Theses
The Department of Computer Science is a discipline concerned with the study of computing, which includes programming, automating tasks, creating tools to enhance productivity, and the understanding of the foundations of computation. The Computer Science program provides the breadth and depth needed to succeed in this rapidly changing field. One of the more recent fields of academic study ...
Computer Science Research Topics (+ Free Webinar)
Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you've landed on this post, chances are you're looking for a computer science-related research topic, but aren't sure where to start.Here, we'll explore a variety of CompSci & IT-related research ideas and topic thought-starters ...
100+ Great Computer Science Research Topics Ideas for 2023
Applications of computer science in medicine. Developments in artificial intelligence in image processing. Discuss cryptography and its applications. Discuss methods of ransomware prevention. Applications of Big Data in the banking industry. Challenges of cloud storage services in 2023.
Undergraduate Research Topics
Available for single-semester IW and senior thesis advising, 2024-2025. Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory. Independent Research Topics: Topics in computational and communication complexity.
500+ Computer Science Research Topics
Computer Science Research Topics are as follows: Using machine learning to detect and prevent cyber attacks. Developing algorithms for optimized resource allocation in cloud computing. Investigating the use of blockchain technology for secure and decentralized data storage. Developing intelligent chatbots for customer service.
Computer Science Thesis: Outline, Topics, Writing Tips
Here are interesting topics for a computer science thesis to review: Discuss databases, data mining, and how cryptocurrency works. Examine the network between neuron network and machine learning. How do robots and computers understand human language. Examine the role of mathematics in modeling computers.
Computer Science and Engineering Theses and Dissertations
Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications, Arup Kanti Dey. PDF. AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, Hamza Elhamdadi. PDF. Automatic Detection of Vehicles in Satellite Images for Economic Monitoring, Cole Hill. PDF
Prize-Winning Thesis and Dissertation Examples
Prize-Winning Thesis and Dissertation Examples. Published on September 9, 2022 by Tegan George.Revised on July 18, 2023. It can be difficult to know where to start when writing your thesis or dissertation.One way to come up with some ideas or maybe even combat writer's block is to check out previous work done by other students on a similar thesis or dissertation topic to yours.
Senior Thesis :: Harvard CS Concentration
Senior Thesis Seminar . Computer Science does not have a Senior Thesis seminar course. However, we do run an informal optional series of Senior Thesis meetings in the Fall to help with the thesis writing process, focused on topics such as technical writing tips, work-shopping your senior thesis story, structure of your thesis, and more.
The Future of AI Research: 20 Thesis Ideas for Undergraduate ...
The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of ...
Computer Science Dissertations and Theses
Theses/Dissertations from 2019. PDF. A Secure Anti-Counterfeiting System using Near Field Communication, Public Key Cryptography, Blockchain, and Bayesian Games, Naif Saeed Alzahrani (Dissertation) PDF. Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series, Logan Blakely (Thesis) PDF.
Computer Science Theses
All of our academic staff are research active, working with a team of post-graduate and post-doctoral researchers and a lively population of research students. Our research focuses on core themes of theoretical and practical computer science: artificial intelligence and symbolic computation, networked and distributed systems, systems ...
Computer Science and Engineering Theses, Projects, and Dissertations
learn programming in virtual reality? a project for computer science students, benjamin alexander. pdf. lung cancer type classification, mohit ramajibhai ankoliya. pdf. high-risk prediction for covid-19 patients using machine learning, raja kajuluri. pdf. improving india's traffic management using intelligent transportation systems, umesh ...
10 Computer Networking Dissertation Topics
Topic 1: An evaluation of the network security during machine to machine communication in IoT. Research Aim: The research aims to evaluate the network security issues associated with M2M communication in IoT. Objectives: To evaluate the factors affecting the network security of IoT devices. To determine the methods for increasing data integrity ...
The M.S. Thesis Track
The M.S. Thesis Track. The MS Thesis track is for students who want to concentrate on research in some sub-field of Computer Science. You are required to arrange for a Computer Science Faculty member who agrees to advise the thesis and the rest of your course selection prior to selecting the track. ... including the free flow of ideas and ...
Computer Science Theses and Dissertations
Theses/Dissertations from 2022. PDF. The Design and Implementation of a High-Performance Polynomial System Solver, Alexander Brandt. PDF. Defining Service Level Agreements in Serverless Computing, Mohamed Elsakhawy. PDF. Algorithms for Regular Chains of Dimension One, Juan P. Gonzalez Trochez. PDF.
Computer Science Library Research Guide
How to search for Harvard dissertations. DASH, Digital Access to Scholarship at Harvard, is the university's central, open-access repository for the scholarly output of faculty and the broader research community at Harvard.Most Ph.D. dissertations submitted from March 2012 forward are available online in DASH.; Check HOLLIS, the Library Catalog, and refine your results by using the Advanced ...
Computer Vision really cool ideas for a thesis? : r/computervision
Your thesis could be based on UI and computer vision as they really are changing the land scape and help an open source project in the process. We also want to add image homography and feature tracking to the next release (1.3). We have quick release cycles as well (about every 3 months).
Theses
A list of completed theses and new thesis topics from the Computer Vision Group. Are you about to start a BSc or MSc thesis? Please read our instructions for preparing and delivering your work. PhD Theses Master Theses Bachelor Theses Thesis Topics. Novel Techniques for Robust and Generalizable Machine Learning. PDF Abstract.
Top 100+ Computer Engineering Project Topics [Updated]
Top 100+ Computer Engineering Project Topics [Updated] Computer engineering projects offer a captivating blend of creativity and technical prowess, allowing enthusiasts to dive into a world where innovation meets functionality. Whether you're fascinated by hardware design, software development, networking, or artificial intelligence, there ...
Electrical & Computer Engineering Masters Theses Collection
6:1 PUMA Arrays: Designs and Finite Array Effects, Michael Lee, Electrical & Computer Engineering. PDF. Protecting Controllers against Denial-of-Service Attacks in Software-Defined Networks, Jingrui Li, Electrical & Computer Engineering. PDF. INFRASTRUCTURE-FREE SECURE PAIRING OF MOBILE DEVICES, Chunqiu Liu, Electrical & Computer Engineering. PDF
Bachelor and Master Thesis : Professorship of Data Science
Q1: How many pages do I need to write? A: In general, the number of pages is only a poor indicator of the quality of a thesis. However, as a rule of thumb, bachelor theses should have around 30 pages, while master theses should be around 60 pages of main content (that is, without the appendix and lists of tables, symbols, figures, references etc.).
180+ Presentation Topic Ideas [Plus Templates]
But for some specific ideas, consider pulling these design tactics into your presentation. Slide Background Ideas: Set photos as your slide background; Use color overlays to make sure your content is still visible on top of the photo background; Create a gradient background; Use a stock video as your background to create motion

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Computer Science Graduate Projects and Theses

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100 Great Computer Science Research Topics Ideas for 2023

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Undergraduate Research Topics

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