Recent Announcements
- To secure admissions to the CDS Department, please apply at the IISc Research Admissions Application Portal (https://admissions.iisc.ac.in/) by March 23, 2026
Overview
The 2026 Research Admission (Ph.D. and M.Tech (Res)) admission process for the Department of Computational and Data Sciences (CDS) involves a two phase selection process for all 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).
Phase I of the in-person interview will involve a written/digital aptitude test (details to be announced by April). On successful selection from the Phase I, a Phase II oral interview will take place immediately afterwards (in the same session as indicated in your call letter).
The syllabus for the Phase I Aptitude Test is as follows:
Syllabus: Combinatorics · Linear Algebra · Probability & Statistics · Differential Equations · Data Structures & Algorithms
The syllabus for the oral interviews are as follows:
Basic topics: Linear Algebra · Probability & Statistics · Programming & Algorithms · Numerical Methods · ODEs · Discrete Mathematics
Advanced topics: Based on your selected labs (up to 3 labs chosen in advance).
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 information form (To be shared). 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.
List of labs that are admitting students
The following labs (AiREX, ATCG, BioMedIA, BCL, CSPL, CMG, FLAME, MATRIX, SCL, QUEST) 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 advanced algorithms, data structures and open-source software tools. 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: Algorithm design and analysis, data structures, graph algorithms.
Students are encouraged to review fundamental data structures (such as arrays, linked lists, stacks, queues, trees, etc.) and algorithms (such as sorting, searching, greedy algorithms, divide and conquer, graph algorithms, etc.) from the Introduction to Algorithms (CLRS) textbook. Students should be able to clearly articulate and explain their algorithmic approaches.
Prior training: Students should have an engineering or an applied mathematics background. Strong programming and algorithm development skills are required.
2. Biomedical Image Analysis (BioMedIA) Lab
Faculty: Vaanathi Sundaresan
Website: https://sites.google.com/view/biomedia-lab/home
Lab Description: Our aim is to develop innovative AI-based methods for computational analysis of multidisciplinary biomedical images for clinical applications. We specifically focus on building scalable and translatable machine learning tools for big data applications. We are also interested in tackling key challenges of medical image analysis including label scarcity and data diversity, long-tail distribution, model generalisability and interpretability, fairness in AI and Bias mitigation.
Our current research areas include computer vision and machine learning-based methods for identification of MR imaging biomarkers for various neurological diseases and their population-level impact, multimodal federated learning, semi-supervised techniques for analysis of various imaging modalities, data harmonisation/domain adaptation, image reconstruction and quality improvement.
Interview topics: Signal Processing, Image Processing, Linear Algebra, Probability and Machine Learning basics.
Prior Training: We are looking for students with an engineering background, preferably Computer Science or Electrical, Electronics and/or Communications, with strong programming skills and interdisciplinary training, interested in the following topics: deep learning and computer vision applications, image processing and medical imaging.
3. 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 (including digital health), 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.
For the 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.
4. Computation, Statistics & Physics Lab (CSPL)
Faculty: Murugesan Venkatapathi
Website: https://cds.iisc.ac.in/faculty/murugesh/lab_html/
Lab description: Current interests at CSPL can be broadly grouped into (a) Theoretical and computational models in condensed matter physics, (b) Randomized algorithms and statistical computing, and (c) Numerical m.
CSPL is looking for a PhD student to work in (a). We are also open to M.Tech (Research) students in areas (b) and (c) above.
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.
5. FLAME:Lab (FLow Analysis and Multi-physics simulations at Extreme-scale) Lab
Faculty: Konduri Aditya
Website: https://cds.iisc.ac.in/faculty/konduriadi/ and https://flamelab-iisc.github.io
Lab description: The research group focuses on the simulation and analysis of multi-scale multi-physics fluid flow problems that leverage high performance computing (HPC) platforms and machine learning (ML). Specifically, the work would involve development of HPC centric numerical methods and algorithms for solving partial differential equations (PDE) that are relevant to computational fluid dynamics (CFD), application of machine learning methods to analyze and model data generated from simulations, and investigation of turbulent flow problems that arise in combustion systems (gas turbine and scramjet engines), high-speed aerodynamics and environmental flows.
Our current projects include machine learning based reduced-order modelling (ROM) for reacting flows, scalable asynchronous PDE solvers, hydrogen combustion, and detection & forecasting of extreme events. Please visit our lab website for more details. For the Aug 2025 admissions, we are looking for three PhD/MTech (Research) students.
Interview topics: Numerical methods, linear algebra, probability, scientific programming, differential equations, fluid mechanics (optional), programming models (optional)
Prior training: Students should have a Bachelors’ or Masters’ degree in any one of the following areas: Mechanical Engineering, Aerospace Engineering, Chemical Engineering, Computational Engineering, Computational Physics, Applied Mathematics, Scientific computing or similar areas. Ph.D. candidates are preferred.
6. 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, cutting across quantum mechanics, atomistics, continuum mechanics, numerical linear algebra, finite element methods, machine learning and large-scale scientific computing using multi-node CPUs+GPUs. In particular, we focus on developing advanced mathematical techniques, hardware-aware computational algorithms, multiscale methodologies, and data-driven approaches aimed at pushing the boundaries of the current predictive capabilities of materials design. Additionally, students will contribute to novel HPC-centric computational methodologies and algorithmic strategies, that will integrate into the massively parallel open-source code DFT-FE, the workhorse behind the 2023 ACM Gordon Bell Prize (world’s prestigious prize in high performance computing). The research you conduct will truly reflect our country’s indigenous ability to build methods and open-source exascale codes, ensuring our nation remains competitive in this exascale era.
Our lab is also working on developing quantum computing-based algorithms for accelerating scientific computations targeting large-scale eigenproblems, partial differential equations arising in various areas of science and engineering.
In Aug 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.
7. Scientific Computation Lab (SCL)
Faculty: Soumyendu Raha
Website: https://cds.iisc.ac.in/faculty/raha/
Lab description: The Scientific Computation Lab works on mathematical and computational aspects of Data Science and on Computational Mathematics. Current research interests are in Computational aspects of Stochastic Differential Equations, application of constrained dynamical systems, construction of stochastic networks and complexes of simplices for learning from data obtained from sensing/measurement of dynamical systems and prediction of responses of the stochastic dynamical systems. Application interests include but are not limited to very long-range surveillance of near space objects, power drop in VLSI physical designs, flow problems, non-linear particle filters and homotopic algorithms on graph networks and on complexes of simplices for learning and prediction of behaviour of time series and/or complex systems.
Interview topics: Matrix Analysis, programming in a compiled (preferable) and/or interpreted language, computer implementation (basic data structure and foundational algorithms) of graph and/or linear algebra algorithms, probability theory. Familiarity with dynamical systems/ordinary differential equations (ODE) is a plus.
Prior training: Any advanced engineering mathematics book such as Kreyszig etc. for matrix analysis, probability and ODEs; any book on introductory programming in C/python with data structures used in first semesters of engineering courses. Interview should reflect sound and in-depth prior training in these simple but foundational areas.
We are looking for one doctoral student.
8. QUEST Lab
Faculty: Deepak N. Subramani
Website: https://cds.iisc.ac.in/faculty/deepakns/
Lab description: QUEST Lab, conducts interdisciplinary research in Scientific Machine Learning and AI for Science and Engineering, balancing method development and practical applications. Currently our interest is in multi-modal AI, foundation models for scientific data, physical intelligence and neural data assimilation. Academically well versed and highly competitive candidates with interest in our research and ability to focus for long hours are welcome irrespective of your background. We especially welcome students with interdisciplinary interests (for example, a Bachelor’s degree in Mechanical/Aerospace/Civil/ECE/EE with self-learned programming (Deep Learning projects) knowledge).
Interview topics: Applied Probability, Numerical Linear Algebra, General Aptitude (we will specifically test for your ability to operate with limited information, probing you on how you overcome adversity), Basics of Deep Learning, Algorithmic Thinking (How you would instruct a digital computer for performing regular tasks).
Prior training: Any bachelor’s degree is welcome. Interdisciplinary interest is strongly required. Strong programming skills beyond simple prompting in free versions of Chatbots is a must.
9. AI for Research and Engineering eXcellence (AiREX)
Faculty: Sashikumaar Ganesan
Website: https://cds.iisc.ac.in/faculty/sashi/
Lab description: The AI for Research and Engineering eXcellence (AiREX) Lab at CDS, IISc Bangalore, pioneers cutting-edge research at the intersection of Artificial Intelligence, Scientific Machine Learning (SciML), and Computational Sciences. Dedicated to solving complex scientific and engineering problems, the lab specializes in AI-driven predictive modeling, finite element methods, deep learning, generative AI, and SciMLOps. By blending traditional scientific methods with modern AI technologies, AiREX Lab aims to drive scientific discovery and technological advancement, shaping the future of computational research.
Who can opt? AiREX lab is seeking highly motivated PhD students to work on SciML, particularly in the area of Physics-informed neural networks (PINNs). For more details, check the latest publication at (https://cds.iisc.ac.in/faculty/sashi/).
Interview topics: Numerical methods, Linear algebra, Differential equations, Could computing, Machine Learning, Generative AI, MLOps.
Prior training: Candidates must be proficient in C/C++/Python programming.
The following labs (CSL, DREAM, DSL, MARS, NLP, VAL, VCL) only accept students from CS/ECE/EE background qualification.
1. Cloud Systems Lab (CSL)
Faculty: J. Lakshmi
Website: http://www.serc.iisc.ac.in/faculty/jlakshmi and http://www.serc.iisc.ac.in/faculty/jlakshmi/cloud-system-lab
Lab Description: Cloud Systems Lab (CSL) engages in large-scale distributed systems research, specifically in the emerging and evolving paradigms of service architectures, isolation, resource provisioning and management, virtualization, guarantees (QoS) for application performance, dependability, fault tolerance, etc. Incumbent students are required to have good background knowledge of Operating system topics like resource management, scheduling, process and memory management, disks, and file systems, I/O devices, interrupt management, networking, synchronization, deadlocks, agreement protocols, etc., and passion for systems research.
Current research work entails architecting and evaluating serverless platforms for QoS, realizing lightweight virtualization environment for real-time DAG based workflows, Service resilience using data-oriented recovery paradigms, Intelligent solutions for real-time dynamic optimal placement problems.
We are interested in, but not limited to, exploring disaggregated resources for distributed computing, stateful serverless paradigm, and software defined data centers. Recent works have explored and applied AI/ML methods to solving Systems challenges.
Interview topics: Operating Systems, Distributed Systems, Computer Organization
Prior training: Students must have a Bachelors’ or Masters’ degree in Computer Science, Information Technology, Electrical, Electronics and/or Communications. Preference will be given to those having experience with any OS or Linux source code.
2. 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 2025 Admissions cycle, the DREAM:Lab is looking for 4-5 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:
1. Operating System Concepts by Silberschatz, Gavin and Gagne
2. Distributed Systems: Concepts and Design by Coulouris, et al.
3. TiFL: A Tier-based Federated Learning System, Zheng Chai, et al, HPDC, 2020 https://arxiv.org/abs/2001.09249
4. 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.
3. 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.
Recent projects include LLM-based Relational Query Optimization, GPU-based Relational Query Processing, Database Index Optimization on Quantum Platforms, Active Learning for Hidden Query Extraction, Robust Query Processing.
Interview topics: Data Models, Query Languages, Schema Design, Transaction Processing, Index Structures, Memory Management (essentially DBMS syllabus of GATE exam)
Prior training: Bachelor’s or Master’s in Computer Science/Information Technology, and creative aptitude for algorithms and computer systems.
4. 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 for AI/ML applications.
· Novel models of parallelism for Machine Learning (ML)/Deep Learning (DL) applications.
· High performance parallel I/O solutions for scientific and ML/DL applications.
· Fault tolerance frameworks for parallel scientific and ML/DL applications.
Interview topics:
- Operating Systems including pthreads, filesystems.
2. 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.)
3. 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. Ph.D. candidates are preferred.
5. 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.
6. Vision and AI Lab (VAL)
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 ii) Domain Adaptation and Generalization iii) Adversarial Attacks and Defenses iv) Self-supervised and unsupervised learning v) 3D Vision/Gaussian Splatting vi) Learning on long-tail data vii) Generative models viii) Multi-modal learning ix) Fairness and Bias.
Interview topics: Linear Algebra, Probability, Machine Learning and Image Processing/Computer Vision (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.
7. 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: 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.
