phd thesis in machine learning

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• AE 8803 Gaussian Processes
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6402 , Time Series Analysis
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6221 , Probability Theory for Scientists and Engineers
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• AE 8803 , Optimal Transport Theory and Applications
• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6241 , Probability I
• MATH 6262 , Advanced Statistical Inference
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 6790 / ECE 6790 , Information Processing Models in Neural Systems
• BMED 7610 , Quantitative Neuroscience
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CHBE 6745 , Data Analytics for Chemical Engineers
• CHBE 6746 , Data-Driven Process Engineering
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7647 , Machine Learning with Limited Supervision
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• AE 6513 , Mathematical Planning and Decision-Making for Autonomy
• AE 8803 , Optimization-Based Learning Control and Games
• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ECE 6553 , Optimal Control and Optimization
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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Social and Affective Machine Learning

Nov. 19, 2019

• Natasha Jaques Former Research Assistant

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Jaques, Natasha. Social and Affective Machine Learning. 2019. Massachusetts Institute of Technology, PhD dissertation.

Social learning is a crucial component of human intelligence, allowing us to rapidly adapt to new scenarios, learn new tasks, and communicate knowledge that can be built on by others. This dissertation argues that the ability of artificial intelligence to learn, adapt, and generalize to new environments can be enhanced by mechanisms that allow for social learning. I propose several novel deep- and reinforcement-learning methods that improve the social and affective capabilities of artificial intelligence (AI), through social learning both from humans and from other AI agents. First, I show how AI agents can learn from the causal influence of their actions on other agents, leading to enhanced coordination and communication in multi-agent reinforcement learning. Sec- ond, I investigate learning socially from humans, using non-verbal and implicit affective signals such as facial expressions and sentiment. This ability to optimize for human satisfaction through sensing implicit social cues can enhance human-AI interaction, and guide AI systems to take actions aligned with human preferences. Learning from human interaction with reinforcement learning, however, may require dealing with sparse, off-policy data, without the ability to explore online in the environment – a situation that is inherent to safety-critical, real-world systems that must be tested before being deployed. I present several techniques that enable learning effectively in this challenging setting. Experiments deploying these models to interact with humans reveal that learning from implicit, affective signals is more effective than relying on humans to provide manual labels of their preferences, a task that is cumbersome and time-consuming. However, learning from humans’ affective cues requires recognizing them first. In the third part of this thesis, I present several machine learning methods for automatically interpreting human data and recognizing affective and social signals such as stress, happiness, and conversational rapport. I show that personalizing such models using multi-task learning achieves large performance gains in predicting highly individualistic outcomes like human happiness. Together, these techniques create a framework for building socially and emotionally intelligent AI agents that can flexibly learn from each other and from humans. 

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Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P. A., Strouse, D. J., Leibo, J. Z., and de Freitas, N. "Intrinsic Social Motivation via Causal Influence in Multi-Agent RL," International Conference on Representation Learning (ICLR), New Orleans, Louisiana, May 2019 (submitted).

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10 Compelling Machine Learning Dissertations from Ph.D. Students

Data Science Academic Research Featured Post Academia Machine Learning Research posted by Daniel Gutierrez, ODSC June 18, 2019 Daniel Gutierrez, ODSC

As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and fertile areas of research. Other sources of valuable research developments are in the form of Ph.D. dissertations, the culmination of a doctoral candidate’s work to confer his/her degree. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. In this article, I present 10 compelling machine learning dissertations that I found interesting in terms of my own areas of pursuit. I hope you’ll find several of them that match your own interests. Each thesis may take a while to consume but will result in hours of satisfying summer reading. Enjoy!

[Related Article: The Best Machine Learning Research of 2019 So Far ]

1. Recognition of Everyday Activities through Wearable Sensors and Machine Learning

Over the past several years, the use of wearable devices has increased dramatically, primarily for fitness monitoring, largely due to their greater sensor reliability, increased functionality, smaller size, increased ease of use, and greater affordability. These devices have helped many people of all ages live healthier lives and achieve their personal fitness goals, as they are able to see quantifiable and graphical results of their efforts every step of the way (i.e. in real-time). Yet, while these device systems work well within the fitness domain, they have yet to achieve a convincing level of functionality in the larger domain of healthcare.

The goal of the research detailed in this dissertation is to explore and develop accurate and quantifiable sensing and machine learning techniques for eventual real-time health monitoring by wearable device systems. To that end, a two-tier recognition system is presented that is designed to identify health activities in a naturalistic setting based on accelerometer data of common activities. In Tier I a traditional activity recognition approach is employed to classify short windows of data, while in Tier II these classified windows are grouped to identify instances of a specific activity.

2. Algorithms and analysis for non-convex optimization problems in machine learning

This dissertation proposes efficient algorithms and provides theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, the focus is on two types of non-convex optimization problems: learning the parameters of latent variable models and learning in deep neural networks. Learning latent variable models is traditionally framed as a non-convex optimization problem through Maximum Likelihood Estimation (MLE). For some specific models such as multi-view model, it’s possible to bypass the non-convexity by leveraging the special model structure and convert the problem into spectral decomposition through Methods of Moments (MM) estimator. In this research, a novel algorithm is proposed that can flexibly learn a multi-view model in a non-parametric fashion. To scale the nonparametric spectral methods to large datasets, an algorithm called doubly stochastic gradient descent is proposed which uses sampling to approximate two expectations in the problem, and it achieves better balance of computation and statistics by adaptively growing the model as more data arrive. Learning with neural networks is a difficult non-convex problem while simple gradient-based methods achieve great success in practice. This part of the research tries to understand the optimization landscape of learning one-hidden-layer networks with Rectified Linear (ReLU) activation functions. By directly analyzing the structure of the gradient, it can be shown that neural networks with diverse weights have no spurious local optima.

3. Algorithms, Machine Learning, and Speech: The Future of the First Amendment in a Digital World

We increasingly depend on algorithms to mediate information and thanks to the advance of computation power and big data, they do so more autonomously than ever before. At the same time, courts have been deferential to First Amendment defenses made in light of new technology. Computer code, algorithmic outputs, and arguably, the dissemination of data have all been determined as constituting “speech” entitled to constitutional protection. However, continuing to use the First Amendment as a barrier to regulation may have extreme consequences as our information ecosystem evolves. This research focuses on developing a new approach to determining what should be considered “speech” if the First Amendment is to continue to protect the marketplace of ideas, individual autonomy, and democracy.

4. Deep in-memory computing

There is much interest in embedding data analytics into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and Internet-of-Things to provide these with decision-making capabilities. Such platforms often need to implement machine learning (ML) algorithms under stringent energy constraints with battery-powered electronics. Especially, energy consumption in memory subsystems dominates such a system’s energy efficiency. In addition, the memory access latency is a major bottleneck for overall system throughput. To address these issues in memory-intensive inference applications, this dissertation proposes deep in-memory accelerator (DIMA), which deeply embeds computation into the memory array, employing two key principles: (1) accessing and processing multiple rows of memory array at a time, and (2) embedding pitch-matched low-swing analog processing at the periphery of bitcell array.

5. Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms

Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such data sets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered: (a) Sparse data and convergence behavior. How different properties of a data set, such as the sparsity rate and the mechanism of missing data systematically affect convergence behavior of classification? (b) Handling sparse data with non-linear model. How to efficiently learn non-linear data structures when classifying large sparse data? This dissertation attempts to address these questions with empirical and theoretical analysis on large and sparse data sets.

6. Collaborative detection of cyberbullying behavior in Twitter data

As the size of Twitter data is increasing, so are undesirable behaviors of its users. One such undesirable behavior is cyberbullying, which could lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, in real-time if possible. Prevalent approaches to identifying cyberbullying are mainly stand-alone, and thus, are time-consuming. This dissertation proposes a new approach called distributed-collaborative approach for cyberbullying detection. It contains a network of detection nodes, each of which is independent and capable of classifying tweets it receives. These detection nodes collaborate with each other in case they need help in classifying a given tweet. The study empirically evaluates various collaborative patterns, and it assesses the performance of each pattern in detail. Results indicate an improvement in recall and precision of the detection mechanism over the stand- alone paradigm.

7. Bringing interpretability and visualization with artificial neural networks

Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art training techniques, while taking much less time to train a model. Experiments show that the speedup of training ELM is up to the 5 orders of magnitude comparing to standard Error Back-propagation algorithm. ELM is a recently discovered technique that has proved its efficiency in classic regression and classification tasks, including multi-class cases. In this dissertation, extensions of ELMs for non-typical for Artificial Neural Networks (ANNs) problems are presented.

8. Scalable Manifold Learning and Related Topics

The subject of manifold learning is vast and still largely unexplored. As a subset of unsupervised learning it has a fundamental challenge in adequately defining the problem but whose solution is to an increasingly important desire to understand data sets intrinsically. It is the overarching goal of this work to present researchers with an understanding of the topic of manifold learning, with a description and proposed method for performing manifold learning, guidance for selecting parameters when applying manifold learning to large scientific data sets and together with open source software powerful enough to meet the demands of big data.

9. The Intelligent Management of Crowd-Powered Machine Learning

Artificial intelligence and machine learning power many technologies today, from spam filters to self-driving cars to medical decision assistants. While this revolution has hugely benefited from algorithmic developments, it also could not have occurred without data, which nowadays is frequently procured at massive scale from crowds. Because data is so crucial, a key next step towards truly autonomous agents is the design of better methods for intelligently managing now-ubiquitous crowd-powered data-gathering processes. This dissertation takes this key next step by developing algorithms for the online and dynamic control of these processes. The research considers how to gather data for its two primary purposes: training and evaluation.

[Related Article: 25 Excellent Machine Learning Open Datasets ]

10. System-Aware Optimization for Machine Learning at Scale

New computing systems have emerged in response to the increasing size and complexity of modern datasets. For best performance, machine learning methods must be designed to closely align with the underlying properties of these systems. This dissertation illustrates the impact of system-aware machine learning through the lens of optimization, a crucial component in formulating and solving most machine learning problems. Classically, the performance of an optimization method is measured in terms of accuracy (i.e., does it realize the correct machine learning model?) and convergence rate (after how many iterations?). In modern computing regimes, however, it becomes critical to additionally consider a number of systems-related aspects for best overall performance. These aspects can range from low-level details, such as data structures or machine specifications, to higher-level concepts, such as the tradeoff between communication and computation. We propose a general optimization framework for machine learning, CoCoA, that gives careful consideration to systems parameters, often incorporating them directly into the method and theory.

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Probabilistic Machine Learning in the Age of Deep Learning: New Perspectives for Gaussian Processes, Bayesian Optimization and Beyond (PhD Thesis)

Advances in artificial intelligence (AI) are rapidly transforming our world, with systems now matching or surpassing human capabilities in areas ranging from game-playing to scientific discovery. Much of this progress traces back to machine learning (ML), particularly deep learning and its ability to uncover meaningful patterns and representations in data. However, true intelligence in AI demands more than raw predictive power; it requires a principled approach to making decisions under uncertainty. This highlights the necessity of probabilistic ML, which offers a systematic framework for reasoning about the unknown through probability theory and Bayesian inference. Gaussian processes (GPs) stand out as a quintessential probabilistic model, offering flexibility, data efficiency, and well-calibrated uncertainty estimates. They are integral to many sequential decision-making algorithms, notably Bayesian optimisation (BO), which has emerged as an indispensable tool for optimising expensive and complex black-box objective functions. While considerable efforts have focused on improving gp scalability, performance gaps persist in practice when compared against neural networks (NNs) due in large to its lack of representation learning capabilities. This, among other natural deficiencies of GPs, has hampered the capacity of BO to address critical real-world optimisation challenges. This thesis aims to unlock the potential of deep learning within probabilistic methods and reciprocally lend probabilistic perspectives to deep learning. The contributions include improving approximations to bridge the gap between GPs and NNs, providing a new formulation of BO that seamlessly accommodates deep learning methods to tackle complex optimisation problems, as well as a probabilistic interpretation of a powerful class of deep generative models for image style transfer. By enriching the interplay between deep learning and probabilistic ML, this thesis advances the foundations of AI and facilitates the development of more capable and dependable automated decision-making systems.

The full text is available as a single PDF file

You can also find a list of contents and PDFs corresponding to each individual chapter below:

Table of Contents

• Chapter 1: Introduction
• Chapter 2: Background
• Chapter 3: Orthogonally-Decoupled Sparse Gaussian Processes with Spherical Neural Network Activation Features
• Chapter 4: Cycle-Consistent Generative Adversarial Networks as a Bayesian Approximation
• Chapter 5: Bayesian Optimisation by Classification with Deep Learning and Beyond
• Chapter 6: Conclusion
• Appendix A: Numerical Methods for Improved Decoupled Sampling of Gaussian Processes
• Bibliography

Please find Chapter 1: Introduction reproduced in full below:

Introduction

PhD Candidate (AI & Machine Learning)

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• Princeton University Doctoral Dissertations, 2011-2024
• Operations Research and Financial Engineering

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Latest phd thesis topics in machine learning.

• With progressive technological development, the exploration of machine learning has increased a huge number of applications. Consequentially. Machine learning instigates an essential part of implementing smart and automated applications by intelligent data analysis.
• The applicability of machine learning is abundant in many real-world application fields, such as predictive analytics and intelligent decision-making, cyber-security systems, smart cities, healthcare, e-commerce, agriculture, finance, retail, social media, traffic prediction and transportation, computer vision applications, user behavior analytics and context-aware smartphone applications, bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.
• Recently described branches of machine learning are computational learning theory, adversarial machine learning, quantum machine learning, robot learning, and meta-learning. Efficient data processing and handling the diverse learning algorithms are the constraints that are needed to be the focus in machine learning. PHD thesis on machine learning contributes to the challenges, promising research opportunities, and effective solutions in various application areas. Below is the list of PHD thesis topics on machine learning to effectively explore, scrutinize and discover the new findings of machine learning systems.

List of Sample PHD Thesis in Machine Learning

• Incremental Learning for Large-Scale Data Stream Analytics in a Complex Environment
• Neural Sequential Transfer Learning for Relation Extraction
• Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
• Neural Aspect-based Text Generation
• Leveraging Social Media and Machine Learning for enhanced Communication and Understanding between Organizations and Stakeholders
• Deep Learning Methods for Short,Informal, and Multilingual Text Analytics
• Deep Learning Based Cursive Text Detection and Recognition in Natural Scene Images
• Deep Learning-Based Text Detection and Recognition
• Explaining Deep Neural Networks
• Machine Learning Techniques in Spam Filtering
• Anomaly-Based Network Intrusion Detection Using Machine Learning
• Machine Learning for Financial Products Recommendation
• Sentiment Analysis of Textual Content in Social Networks
• Deep Learning For Time Series Classification
• Deep Learning for Traffic Time Series Data Analysis
• Novel applications of Machine Learning to Network Traffic Analysis and Prediction
• Deep Learning for Animal Recognition
• Neural Transfer Learning for Natural Language Processing
• Scalable and Ensemble Learning for Big Data
• Ensembles for Time Series Forecasting
• Sample-Efficient Deep Reinforcement Learning for Continuous Control
• Towards Generalization and Efficiency in Reinforcement Learning
• Transfer Learning with Deep Neural Networks for Computer Vision
• Deep Learning for Recommender Systems
• CHAMELEON: A Deep Learning Meta-Architecture For News Recommender Systems
• Learning in Dynamic Data-Streams with a Scarcity of Labels
• Learning Meaning Representations For Text Generation With Deep Generative Models
• Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information
• Global-Local Word Embedding for Text Classification
• Measuring Generalization and Overfitting in Machine Learning
• Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions
• Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn
• Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data
• Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data
• An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains
• Deep Learning Applications for Biomedical Data and Natural Language Processing
• Deep Neural Network Models for Image Classification and Regression
• Deep learning for medical report texts
• Deep multi-agent reinforcement learning
• Artificial intelligence methods to support people management in organisations
• An Intelligent Recommender System Based on Short-term Disease Risk Prediction for Patients with Chronic Diseases in a Telehealth Environment
• Bringing Interpretability and Visualization with Artificial Neural Networks
• Investigating machine learning methods in Recommender systems
• Adaptive Machine Learning Algorithms For Data Streams Subject To Concept Drifts
• Active Learning for Data Streams
• Heart Diseases Diagnosis Using Artificial Neural Networks
• Advanced Natural Language Processing and Temporal Mining for Clinical Discovery
• Uncertainty in Deep Learning
• Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems
• Sentiment analysis on students Real-time Feedback
• Aspect-Based Opinion Mining From Customer Reviews
• Word Embeddings for Natural Language Processing
• On Effectively Creating Ensembles of Classifiers
• Design of Intelligent Ensembled Classifiers Combination Methods
• ELSE: Ensemble Learning System with Evolution for Content Based Image Retrieval
• Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks
• Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks
• Using Assessments of Contextual Learning to Identify Characteristics of Adaptive Transfer in Medical Students
• Recursive Deep Learning for Natural Language Processing and Computer Vision
• Machine learning strategies for multi-step-ahead time series forecasting
• General Attention Mechanism for Artificial Intelligence Systems
• Defense Acquisition University: A Study of Employee Perceptions on Web Based Learning Transfer
• Incremental Learning with Large Datasets
• Machine Learning and Data Mining Methods for Recommender Systems and Chemical Informatics
• Transfer of Learning in Leadership Development: Lived Experiences of HPI Practitioners
• Online Ensemble Learning in the Presence of Concept Drift
• Learning From Data Streams With Concept Drift
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Statistical Machine Learning Methods for Modelling, Imaging, and Monitoring the Brain

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• Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)
• Ordinary differential equations, difference equations and dynamical systems
• Biomedical imaging

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PhD defence of Tayeb Meridji – Power System Stability Assessment Frameworks Using Machine-Learning Techniques

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The widespread adoption of renewable energy is causing unpredictability in power networks, making it hard to predict critical operating points. This new reality challenges traditional stability assessment methods still used by most grid operators.

It becomes essential to adopt a systematic approach that encompasses all hourly operating points over the course of a study year. This thesis introduces novel assessment frameworks that leverage machine learning techniques to allow rapid, deterministic time-series assessments of angular transient stability in the context of high renewable penetration. The frameworks not only offer an evaluation of the transient stability of the grid at a high level but also provide the possibility of performing meticulous analyses of emerging trends in the dynamic responses of individual synchronous generators within systems experiencing reduced inertia.

The heavy computational burden associated with such time-series stability assessments are substantially reduced through the strategic use of supervised and unsupervised learning algorithms. A modified version of the Affinity Propagation clustering algorithm is proposed to cluster the subset of all operating points of a given study year and derive a representative subset of these points. In addition, Gradient Boosting Regressors are also used to predict transient stability indices for all hours of the studied year. And finally, an agglomerative hierarchical clustering algorithm is proposed to cluster synchronous generators based on their dynamic response.

The proposed frameworks are demonstrated to be ideal for grid planners in identifying pathways to achieve reliable integration of renewable energy resources. Through a series of case studies, these frameworks were evaluated to determine the transient stability performance of an IEEE-39 test system augmented with renewable energy resources.

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February 9, 2024

On average, it takes over one decade to develop a new drug. What if machine learning (ML) and statistical computations could speed up the process? This is what Dr. Hanrui Zhang set out to do as her vision for her professional path shaped up during her studies in the Bioinformatics Graduate Program. Dr. Zhang defended her dissertation titled “Predict Drug Response by Machine Learning” on January 25, 2024, under the mentorship of professor Yuanfang Guan. This dissertation explores the application of ML algorithms in surmounting fundamental challenges in drug development, including stabilizing high-throughput screening outcomes and transforming initial discoveries into clinical practices.

There are many applications for this research with a strong potential for tremendous impact. For example, in cancers, the tumorous cells often mutate in response to a drug, requiring to change drugs often, and therefore to constantly develop treatment options. Machine learning can accelerate this process because it can predict the hot spots for mutations and resistance to a drug. With this individualized knowledge, scientists can develop compounds that can target these areas. Being able to safely develop new drugs more quickly would save the lives of millions of cancer patients. 

Dr. Guan’s lab develops ML methods for drug discovery, and this is where Dr. Zhang had the opportunity to participate in a drug-discovery study with Merck, a pharmaceutical company. This scientific collaboration is researching ways to use ML to discover drugs that can target DNA Damage Response (DDR) pathways in tumorous cells. She was very interested in glimpsing at industry R&D processes, and in gaining some understanding of how data meta-learning can be used to develop drugs.

Dr. Zhang also contributed to a study of Parkinson’s disease, using a smartphone to detect and record individual movements. The early onset of the abnormal movements that are the hallmark of this disease can be detected by a smartphone. However, the phone can also be tossed around, for example in a purse, recording noise data that could invalidate the results. To address this issue, Drs. Guan and Zhang developed a data augmentation method that adds action noise to cancel the irrelevant information for a valid prediction. This method earned them first place in the Parkinson’s Disease Digital Biomarker DREAM Challenge out of over one hundred competitors. Working on this project was one of the highlights of Dr. Zhang at U-M. She then realized that there is an intuitive aspect to research, based on a deep understanding of the data and the biology behind it.

 “You push 'something' a little bit, and it can make a big difference. There is a great feeling of achievement when it works.”

During her undergraduate studies, Zhang was an intern at Memorial Sloan Kettering Cancer Center (MSK), as an exchange student from Sun Yat-sen University in Guangzhou, China. At MSK, she was encouraged to study at the University of Michigan (U-M) because of its Bioinformatics Graduate Program high ranking reputation and excellence in bioinformatics. “I’ve always wanted to do research, and the U-M Master’s program is very research-oriented,” she said. “And it's cold in Michigan, that’s good for research!” 

Asked about her passion for bioinformatics, she said: “I’ve always wanted to do creative work to help human beings and I started with computer science in middle school. This is what I like and what I want to do!” When she first arrived at U-M, although she knew what she liked, she had very little idea about what it would become. After over six years of studies at U-M, she now knows what her career is going to look like, and she is very enthusiastic about it.

“I’m at the beginning of my research career and I hope I can do more to treat many diseases.” 

In February 2024, Dr. Zhang is starting a position at the Center for Drug Evaluation and Research at FDA, where she did an internship in summer 2023. As a research scientist and ML/AI reviewer in this division, she will explore using ML to improve the development and regulation process of drugs, such as the treatment for rare diseases.

Outside of work, she likes going hiking and exploring the Bay Area where she relocated. And she has very fond memories of U-M where she also met her fiancé, Adam Beneson, a policy specialist at Google, Youtube.

Dr. Hanrui Zhang and  Adam Beneson hiking in the San Bruno Mountains in California. 

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