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.
Overview
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.
Recommended Reading:
- Operating System Concepts by Silberschatz, Gavin and Gagne
- Distributed Systems: Concepts and Design by Coulouris, et al.
- TiFL: A Tier-based Federated Learning System, Zheng Chai, et al, HPDC, 2020 https://arxiv.org/abs/2001.09249
- Pregel: A System for Large-Scale Graph Processing, Malewicz, et al., SIGMOD, 2010, http://people.csail.mit.edu/xchen/parallel-computing/Pregel.pdf
Prior training: Students must have a Bachelors/Masters degree in Computer Science, IT, Electrical, Electronics or Communications. Strong programming, algorithms and systems skills required. Ph.D. candidates preferred.
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.
