phd in data science bangalore

Programme Details

Open From : -
Open Until : -

Admission Queries

Eligibility - 2024

The Doctor of Philosophy programme in Data Science is a distinctive offering that integrates principles from Computer Science, Statistics, and Mathematics. Given the pivotal role of data science and its computational models in decision-making processes across various domains, it has become a highly sought-after field in research, academia, and industry. The Department of Computer Science is dedicated to delivering a comprehensive curriculum that seamlessly combines theoretical research with practical applications in the realm of data science.

This programme is designed to cultivate dynamic researchers capable of designing sophisticated mathematical and statistical models for extracting knowledge from data. By fostering a blend of creativity and innovation, students are equipped with the skills to address complex data research problems, showcasing intellectual excellence in the fields of Computer Science, Mathematics, and Statistics..

Why choose this course?

Integrates Computer Science, Statistics, Mathematics principles for a comprehensive skill set.

Applies data science for real-world impact in diverse domains.

Highly sought-after in research, academia, and industry.

Department ensures a rich curriculum merging theory with practice.

Cultivates dynamic researchers in advanced modeling techniques.

Fosters creativity and innovation in addressing complex data problems., what you will learn.

Master principles of Computer Science, Statistics, and Mathematics for a holistic understanding.
Develop expertise in applying computational models to drive decision-making across diverse domains.
Acquire sought-after skills in data science, relevant in research, academia, and industry.
Navigate a comprehensive curriculum, seamlessly integrating theoretical research with practical applications.
Cultivate proficiency in designing advanced mathematical and statistical models for effective data extraction.
Foster creativity and innovation, enabling the adept resolution of complex data research challenges.

Career prospects

Unlock diverse career paths in research, academia, and industry with sought-after data science skills.
Attain leadership roles, leveraging expertise in Computer Science, Statistics, and Mathematics.
Contribute to decision-making processes across domains with advanced computational modeling skills.
Navigate dynamic roles in data analysis, research, and innovation, shaping the future of technology.
Excel in academia, becoming a thought leader with intellectual prowess in data science disciplines.
Drive innovation in Computer Science, Mathematics, and Statistics, making a lasting impact in diverse sectors.

(Deemed to be University)

Dharmaram College Post, Hosur Road, Bengaluru - 560029, Karnataka, India

Tel: +91 804012 9100 / 9600

Fax: 40129000

Email: [email protected]

Web: http://www. christuniversity.in

EXCELLENCE AND SERVICE

CHRIST (Deemed to be University) is a nurturing ground for an individual's holistic development to make effective contribution to the society in a dynamic environment.

Course Index

Apply for Faculty Positions
Sponsorship
Research Staff Openings
Data management, Computing and Analytics
Software and Runtime Systems
Computational Science and Engineering
Bioinformatics
Degree Programs
BTech Math and Computing
Degree Programs Summary
Mid-year Research Admissions (2024-25)
Course Degree Admissions, 2024
Admissions FAQ
Incoming Students Orientation 2024
Course Descriptions
Course Schedule
M.Tech. Course Requirements
Departmental Curriculum Committee (DCC)
Academic Calendar
Department Wellness Committee
CDS Logo & Identity

Mid-year Research (Ph.D.) Admissions Interview Procedure (2024-25)

Recent update.

All shortlisted applicants must fill out this Google form (due by Nov 16, 23:59 PM). Note that filling out this form is mandatory.

The mid-year research (Ph.D.) admission process for the Department of Computational and Data Sciences (CDS) involves an oral interview for all shortlisted candidates, to be held in-person in the CDS department. Candidates shortlisted for the interview will receive a call letter or email from the IISc Admissions office (please login to the IISc admissions portal and check for the call letter). The syllabus for the interview, including lab-specific readings, is given in detail below.

About one week before the date of interview, the shortlisted candidates will be sent an email from the CDS department to fill out an online student information Google form . This must be completed and submitted in order to participate in the interview. As part of this, candidates should choose up to three labs in the department for which they will be considered. These labs are described below. If successfully admitted, students will be placed in one of these labs to conduct their research.

In-Person Oral Interview

Each shortlisted applicant will be invited to attend the oral interview to be held at the CDS department in the allotted session as per the applicant’s call letter. During this interview, you will be questioned on the following basic topics, and on advanced topics that are based on your lab preferences:

Basic Topics : Linear Algebra; Probability & Statistics; Programming, Data Structures, Algorithms; Numerical Methods; Ordinary Differential Equations; Discrete Mathematics. Final year undergraduate level preparation is required.

Advanced Topics: You will be questioned on the topics related to labs selected in the student information Google form. The topics for each lab and the prior training expected are listed below. Candidates choosing a lab must be prepared on at least one of the lab-related interview topics.

As this interview process is aimed at selecting the best candidates for the CDS Ph.D. program, applicants should prepare well on their fundamentals, come with a focus and knowledge of the research areas they would like to target in their Ph.D., and have clarity on the preferred lab(s) and the kind of work done in them. Review the research topics and the papers from the labs you are interested in.

List of CDS labs that are admitting students during mid-year admissions

The following labs (ATCG, BCL, CSPL, MATRIX) accept students from all background qualifications.

1. Algorithmic Techniques for Computational Genomics (ATCG)

Faculty: Chirag Jain

Website: https://at-cg.github.io

Lab Description: We work at the intersection of computer science and biology. The goal of the lab is to accelerate discovery in life sciences by designing novel algorithms, data structures and open-source software tools. More specifically, we address computational problems associated with the analysis of large-scale datasets comprising DNA and RNA sequences. Refer to the lab website to learn more about ongoing projects.

The lab has active collaborations with industry (Intel Labs, Strand Life Sciences) and academic groups (Genome Institute of Singapore, University of Texas Dallas, IIT Delhi, Center for Brain Research).

Interview topics: Algorithms design and analysis, data structures, graph algorithms

Prior training: Students should have an engineering or an applied mathematics background. Strong programming and algorithm development skills are required.

2. Biomolecular Computation Laboratory (BCL)

Faculty : Debnath Pal

Website: https://cds.iisc.ac.in/faculty/dpal/

Lab description: The aim of the lab is to understand biological data for insights into biological structure, function and processes at multiple scales. The scope of work spans the areas of genomics, proteomics, metabolomics, structural biology, health and disease, methods and algorithms. There is an opportunity to do research problems in real-life projects in cancer, diabetes, neurodegeneration etc., where intensive bio-computational analysis is required. We emphasize robustness of approach, which may include multi-modal strategies, including AI/ML.

For the Jan 2025/Aug 2025 admissions, we are looking for two PhD students to work in the broad areas of health and disease which may involve translational components.

Interview topics : Students are expected to have good programming knowledge and a sound understanding in at least one of the basic subjects at the undergraduate level: Math, Physics or Chemistry. Exposure to data science, bioinformatics and computational biology is desirable but not essential.

Prior training: Students with sound knowledge in any background and enthusiasm for learning biology. Knowledge of programming is desirable.

3. Computational & Statistical Physics Lab (CSPL)

Faculty: Murugesan Venkatapathi

Website: https://cds.iisc.ac.in/faculty/murugesh/lab_html/

Lab description: Research at CSPL can be broadly grouped into a) theoretical and computational physics, b) numerical and scientific computing, and c) randomized algorithms and statistical computing.

CSPL is looking for PhD students to work in the broad areas ‘a’ and ‘c’.

Interview topics : Linear/Matrix algebra, numerical methods, statistics, and topics in physics as appropriate for the research area.

Prior training: A degree in Engineering or Physics or Mathematics.

4. MATRIX:Lab (Materials-physics & Algorithmic Techniques Research In eXtreme-computing)

Faculty: Phani Motamarri

Website: https://cds.iisc.ac.in/faculty/phanim/

Lab description: Our lab delves into interdisciplinary research, drawing ideas from quantum mechanics, computational linear algebra, materials science, solid mechanics, finite-element methods, machine learning, and large-scale scientific computing (HPC). In particular, we focus on developing advanced mathematical techniques, hardware-aware computational algorithms (CPUs+GPUs, Quantum computers), multiscale methodologies, and machine-learning frameworks aimed at pushing the boundaries of the current predictive capabilities of materials design. This opens up avenues for tackling a wide array of scientifically and technologically significant problems previously beyond reach. Application areas include design of energy storage materials, catalytic materials, quantum modeling of complex defects in functional and structural materials, multi-scale modeling in combustion and nano-fluidic applications. Additionally, students will contribute to novel computational methodologies and HPC algorithmic innovations, which will integrate into the open-source code DFT-FE, the workhorse behind the 2023 ACM Gordon Bell Prize. The research you will conduct truly reflects our country’s indigenous ability to build methods and open-source exascale codes, ensuring our nation remains competitive in this exascale era. Further, our lab is making initial forays into developing quantum computing-based algorithms for scientific computations.

For Jan 2025 admissions, MATRIX lab is looking for bright and highly motivated research students to join our lab. We are looking for candidates interested in conducting highly interdisciplinary research cutting across various disciplines as outlined above. This is an exciting opportunity for students from traditional science and engineering backgrounds to engage in multidisciplinary research at the forefront of computational science and data science, thereby acquiring versatile skills.

Interview topics : Numerical methods, Linear Algebra, Calculus, Scientific programming, Differential equations, Topics based on student’s UG/PG background. (for eg: Solid Mechanics, Quantum Physics, Computational Materials, Finite-element methods etc.,)

Prior training: Bachelors’ or a Masters’ degree in any one of the following areas: Engineering Physics/Physics, Mechanical Engineering, Materials Engineering, Chemical Engineering, Computational Engineering, Physics,

Applied Mathematics or similar areas. Strong programming skills is a must.

The following labs (DREAM, DSL, MARS, NLP, VAL, VCL) only accept students from CS/ECE/EE background qualification.

1. DREAM:Lab (Distributed systems Research on Emerging Applications & Machines)

Faculty: Yogesh Simmhan

Website: http://www.dream-lab.in , https://cds.iisc.ac.in/faculty/simmhan

Lab Description: Research in the DREAM:Lab focuses on distributed systems, Big Data platforms, scalable software platforms for ML, and distributed graph algorithms. We explore, design and develop software platforms, data management architectures and distributed algorithms, for efficient and reliable use of distributed computing systems like cloud computing, edge accelerators and quantum computing, with applications to emerging domains like fintech, drones/UAVs, hyperledger/blockchain and smart city/IoT. We emphasize hands-on systems research on real distributed and cloud computing systems, software platforms and large-scale empirical evaluations. We also actively collaborate with IBM Research, NPCI/UPI, University of Massachusetts-Amherst, Cardiff University, University of Melbourne, etc.

For the 2024 Admissions cycle, the DREAM:Lab is looking for enthusiastic students interested in:

Scalable platforms for federated deep learning using GPU-accelerated edge and cloud computing
ML-driven scheduling algorithms and software platforms for serverless multi-cloud computing
Data management, routing strategies and edge+cloud analytics for drone fleet operations
Platforms and algorithms for hybrid quantum computing and cloud computing
Distributed and temporal algorithms for large graphs and GNNs in fintech and IoT
Scalable consensus algorithms and platforms for distributed hyperledgers and blockchain

Students must have strong aptitude and demonstrated skills in systems software, algorithms and programming.

Interview topics: Operating Systems (or) Graph Algorithms (or) Distributed Systems.

2. Database Systems Lab (DSL)

Faculty : Jayant R. Haritsa

Website : http://dsl.cds.iisc.ac.in/

Lab description : We work on the design, implementation and testing of the internals of database engines, and collaborate with major industrial research labs. Our lab’s software is used in many universities and companies worldwide.

Interview topics : Data Models, Query Languages, Schema Design, Transaction Processing, Index Structures, Memory Management

Prior training : Bachelor’s or Master’s in Computer Science/Information Technology, and creative aptitude for algorithms and computer systems.

3. Middleware And Runtime Systems (MARS) Lab

Faculty: Sathish Vadhiyar

Website: https://cds.iisc.ac.in/faculty/vss

Lab Description: High performance computing (HPC), Parallel computing – middleware, system software, algorithms and applications on large-scale parallel computers and GPUs.

Some of the recent research topics in the MARS lab are the following.

Harnessing the power of both CPUs and GPUs in a hybrid manner in modern-day parallel systems.
Novel models of parallelism for Machine Learning (ML)/Deep Learning (DL) applications.
High performance parallel I/O solutions for scientific and ML/DL applications.
Communication-minimization strategies for Exascale applications including development of asynchronous methods, one-sided communications and approximate computing.
Identifying scalability bottlenecks in parallel applications and developing acceleration and scalability improvement techniques.
Fault tolerance frameworks for parallel scientific and ML/DL applications.

Interview topics:

Operating Systems including pthreads, filesystems.
Computer architecture including multi-core CPUs and modern GPUs, differences between shared memory and distributed memory parallel architectures, different network topologies (e.g., ring, mesh etc.)
MPI parallel programming interface – Google for “MPI Complete reference” and read Introduction, Point-to-Point and Collective Communications chapters.

Prior training: Students must have a Bachelors’ or Masters’ degree in Computer Science, Information Technology, Electrical, Electronics and/or Communications. Strong programming, algorithms and systems skills are required.

4. Natural Language Processing (NLP) Lab

Faculty: Danish Pruthi

Website: https://danishpruthi.com/

Lab Description: This new lab broadly works in the areas of Natural Language Processing (NLP) and Machine Learning (ML). These areas already impact our lives—from answering questions we ask, curating content we read online, auto-completing words we are likely to type, to translating text from languages we don’t understand. The lab aims to ensure that such technologies are inclusive, safe and are adopted responsibly. Specifically, we work on projects related to inclusive model evaluation, model control (e.g., editing, de-biasing trained models) and validating data sources (in response to biased or harmful behavior). Additionally, we hope to contribute towards important NLP applications (e.g., content moderation, assisted writing), and engage in understanding their broader societal and ethical implications. From the outset, the lab is envisioned to be diverse, transparent, friendly and highly collaborative.

Interview topics: Language Modeling, Probability and basics of Machine Learning and Deep Learning.

Prior training: Students must have a Bachelors’ or Masters’ degree in Computer Science, Information Technology, Electrical, Electronics and/or Communications or other closely related disciplines.

5. Vision and AI Lab (VAL – formerly Video Analytics Lab)

Faculty: R. Venkatesh Babu

Website: http://val.cds.iisc.ac.in

Lab Description: At VAL, we aim to perform world-class research in the broad fields of Computer Vision and Machine Learning, to push the performance limits on different applications, and ensure their reliability in practical settings. Our research and expertise spans across several areas in Deep Learning for Computer Vision as listed below:

i) Representation Learning iii) Domain Adaptation iv) Adversarial Attacks and Defenses v) Self-supervised and unsupervised learning vi) Out-of-Distribution (OOD) Robustness of Deep Models/ Domain Generalization vii) Object Detection viii) Learning on long-tail data ix) Generative models x) 3D Vision xi) Multi-modal learning.

Interview topics: Linear Algebra, Probability, Basics of Machine Learning and Image Processing (optional)

Prior training: Students must have a Bachelor’s or Master’s degree in either Computer Science, Information Technology, Electrical, Electronics and/or Communications or in any other closely related areas.

6. Visual Computing Lab (VCL)

Faculty: Anirban Chakraborty

Website: http://visual-computing.in/

Lab Description: At VCL, we are interested in developing novel computer vision and machine learning algorithms to solve visual analytics problems arising from real-world applications. The majority of our ongoing research projects can be categorized into one of the following three areas – 1. Data-efficient & privacy-preserving deep learning (zero-shot/few-shot knowledge distillation, continual learning, source-free/unsupervised domain adaptation, data-free adversarial defense, federated learning etc.), 2. Learning across modalities/domains (e.g., text-driven 3D scene editing, text-based image retrieval, sketch-guided localization, visual question answering, domain adaptation etc.) and 3. Perceiving humans and their actions (e.g., person-reidentification, anomaly detection, human gait and pose analysis etc.).

Interview topics: Linear Algebra, Probability and Machine Learning basics. Students are also strongly encouraged to read at least one recent paper from our group (see https://visual-computing.in/publications/ ) that aligns well with their research interests.

Prior training: Students must have a Bachelor’s or Master’s degree in Computer Science, Information Technology, Electrical, Electronics and/or Communications or in any other closely related areas. Applicants with strong mathematical aptitude and good programming skills will be given preference.