Research Admissions Aug 2023

Visit the link for the provisional list of candidates recommended by the department.

Lab Prefamiliarization Slides

Lab Prefamiliarization Video Recording

The admission process for the Aug 2023 cycle to the Department of Computational and Data Sciences (CDS) comprises two phases. Phase I will be an online aptitude test on the HackerEarth platform, which serves as the first part of the evaluation and Phase II will be an in-person oral interview for all candidates who qualified in the aptitude test conducted in Phase I.

Candidates shortlisted for the online aptitude test (Phase I of 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 online aptitude test will be conducted on the same day and session as per the allotted slot in your call letter. The test will be held in the CDS department and you will be provided with a computer to take the test (you need not bring a laptop/computer). You will receive a separate email from the CDS department for your test and instructions such as the test link, rules, guidelines, practice test, etc. This online aptitude test will have multiple-choice and programming questions as per the syllabus below.

The Phase II in-person oral interview is only for students who attempt and qualify in the online aptitude test conducted in Phase I. The syllabus for the interview (similar to the aptitude test, with lab-specific readings) is also given in detail below.

The shortlisted candidates for the online aptitude test have been sent an email from the CDS department to fill out an online student information Google form. This must be completed and submitted to participate in the in-person online aptitude test and interview. As part of this, candidates should choose up to three labs for which they will be considered. These labs are described below.

Online Aptitude Test

The Phase I online objective and programming test will be conducted using the “HackerEarth” online platform on the same day and session as per the allotted slot in your call letter. This test will be held in the CDS department, and the candidates will be provided with a computer to take the online exam. Candidates will be emailed with a link to the online test a few days before the test date. The test will be enabled at the start of your session before the in-person interviews begin. It must be completed in a single sitting. You will need to create a free HackerEarth account to take the test. You MUST use the same email address as the one used for your IISc Admissions Application. Please do this ahead of time. That email will also have another link to let you take a practice test to get familiar with the HackerEarth platform. These practice questions are not from the test syllabus. Candidates may take the practice test as many times as they want. Candidates should get familiar with the programming IDE, compilation, and testing interface of HackerEarth to ensure that the programming questions are correctly attempted.

The online aptitude test will have 8 questions. There will be 6 multiple choice questions (5 marks each) and 2 programming questions (25 marks each). The topics for the objective test are:

  • Combinatorics, Linear Algebra/Matrices, Probability and Statistics, Differential Equations, Plotting, Data Structures and Algorithms.

The programming languages that you can use are C, C++ and Java. The HackerEarth interface will allow you to type the program, compile it and run test cases, all within the browser. We will provide a basic code template for reading and writing the inputs and outputs for each problem. The candidates should focus on the actual code logic and brush up on concepts such as if/then/else conditions, for/while loops and arrays/data structures.

Sample questions from previous year tests are available here.

Students are encouraged to first solve the problems that they are comfortable with and then move on to more difficult problems.

In-Person Oral Interview

Students who qualify in the online aptitude test (Phase I of the interview) will only attend the in-person oral interview (Phase II of the interview) in the allotted slot as per the call letter. In the oral 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; 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.

Note: Most labs give preference to applicants for the Ph.D. program over the M.Tech. (Research) program. Some labs may not even take any students for the M.Tech. (Research) program. Students with a B.E./B.Tech degree are also eligible to apply for the Ph.D. program

Candidates should prepare well on their fundamentals, come with a focus and knowledge of the research areas they would like to target in their PhD, 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 labs that are admitting students in Aug 2023

The following labs (ATCG, BioMedIA, BCL, CSPL, FLAME, MATRIX, NATL, STARS) accept students from all background qualifications

1. Algorithmic Techniques for Computational Genomics (ATCG)

Faculty: Chirag Jain


Lab Description: We work at the intersection of computer science and biology to accelerate scientific discovery by designing novel algorithms, data structures and open-source software tools. We address computational problems associated with analysis of DNA and RNA sequences. Refer to the lab website to learn more about ongoing projects.

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

Prior training: Any degree. Strong programming and algorithm development skills are required.

2. Biomedical Image Analysis (BioMedIA) Lab

Faculty: Vaanathi Sundaresan


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 tools for big data applications in neuroimaging. We are also interested in tackling key challenges of medical image analysis including label scarcity and data diversity, model generalisability and interpretability.

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, 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 Engineering, interested in the following topics: deep learning and computer vision applications, domain adaptation and weakly supervised learning, uncertainty estimation and explainable AI methods in medical imaging, specifically neuroimaging.

3. Biomolecular Computation Laboratory (BCL)

Faculty: Debnath Pal


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.

For the Aug 2023 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. Computational & Statistical Physics Lab (CSPL)

Faculty: Murugesan Venkatapathi


Lab description: Research at CSPL can be broadly grouped into a) theoretical and computational models in condensed matter, b) numerical and scientific computing, c) randomized algorithms and statistical computing, and d) statistical models and data analysis. Typically, PhD students work in one of the three areas a, b or c.

For the Aug 2023 admissions, CSPL is looking for two PhD students to work in the broad areas ‘a’ and ‘c’. In the former, we would like to extend some of our recent understanding of counterintuitive optical properties of extremely small metal nanoparticles to their electrical properties as well. In ‘c’, we are interested in developing new algorithms for randomized numerical linear algebra.

Interview topics: Linear/Matrix algebra, numerical methods, statistics, topics in physics

Prior training: A degree in Engineering or Physics or Mathematics as appropriate for the research topic.

5. FLAME:Lab (FLow Analysis and Multi-physics simulations at Extreme-scale) Lab

Faculty: Konduri Aditya

Website: and

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. 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 reduced-order modelling (ROM) for reacting flows, scalable asynchronous PDE solvers, hydrogen combustion, and detection & forecasting of extreme events.  For the Aug 2023 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) Lab

Faculty:  Phani Motamarri


Lab description: The research in the lab is centered around developing mathematical techniques leading to scalable computational and data-driven methodologies that are aimed at accelerating materials discovery.  The key goal is to push the boundaries of the current predictive capabilities of materials design using quantum mechanical theories opening up the possibility of addressing a broad range of scientifically and technologically important problems that have been out of reach so far. Application areas include the 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.

In Aug 2023 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 applied mathematics, quantum mechanics, materials science, solid mechanics, finite-element methods, numerical linear algebra, machine learning and hardware-aware large-scale scientific computing on multinode CPU and GPU architectures (MPI+CUDA). This is an exciting opportunity for students trained in traditional streams of science and engineering to conduct multidisciplinary research in the cutting-edge areas of computational science and data science, thereby acquiring transferable 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: Mechanical Engineering, Materials Engineering, Chemical Engineering, Computational Engineering, Physics, Applied Mathematics Scientific computing or similar areas. Strong programming skills are a must

7. Numerical Algorithms and Tensor Learning Laboratory (NAT Lab)

Faculty: Ratikanta Behera


Lab Description: We are developing adaptive recurrent neural networks to solve time-varying tensor equations. The aim is to design innovative, scalable, and efficient tensor-based algorithms supported by theoretical principles to solve significant existing and emerging multidimensional problems. Specifically, interested in developing fast tensor algorithms for solutions to multilinear systems, nonlinear optimization problems, low-rank approximation, generalized inverses of tensors, and solutions to partial differential equations in high dimensions problems. We also focus on developing HPC-centric adaptive wavelet algorithms for solving Partial Differential Equations (PDEs), Fractional Differential Equations (FDEs), integral equations, and signal & image processing.

Interview Topics: Linear/Matrix algebra, Numerical Methods, Differential Equations with Basic Programming.

Student Background: Mathematics is preferable. However, we welcome applicants with Physics, Computer Science, Earth Science, Information Technology, Electrical, Electronics and/or Communications, or any other closely related areas who are enthusiastic about learning and developing skills in these areas.

For the 2023 Admissions cycle, the NAT Lab is looking for 2-3 enthusiastic students interested in the following topic.

  1. Tensor Decompositions and Approximations
  2. Generalized inverses for Matrix/Tensor
  3. Neural Networks and its Applications
  4. Image/Video Processing
  5. Wavelet Methods for (Ordinary/Partial/Fractional) Differential Equations
  6. Wavelets in Scientific Computing

8. Scientific Machine Learning and Operations (STARS)

Faculty: Sashikumaar Ganesan


Lab description: Scientific Machine Learning (ScML) has the potential to revolutionize how we solve partial differential equations (PDEs). However, realizing this potential requires overcoming several challenges. Our research group is focused on addressing these challenges and making ScML a widely used tool in scientific computing. 

Our research plan includes improving the generality and computational efficiency of Neural Networks, as well as addressing time-dependence handling, and dynamic boundary and interior layer handling. We will also incorporate Finite Element Methods, MLOps, and digital twin techniques to enhance efficiency, scalability, and reliability. 

Furthermore, we plan to apply ScML schemes to computational fluid dynamics (CFD) to improve the accuracy of fluid flow simulations. Our ultimate goal is to transform our open source finite element package ParMooN into an open source ScML package, contributing substantially to scientific machine learning and revolutionizing how PDEs are solved.

Interview topics: Linear Algebra or Numerical Methods or Basics of Machine Learning or Parallel programming

Prior training: While prior knowledge of modeling, simulation, and programming is preferred, it is not required. We welcome applicants with any degree background who are enthusiastic about learning and developing skills in these areas.

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

1. Cloud Systems Lab (CSL)

Faculty: J. Lakshmi

Website: and

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.

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.

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

Faculty: Yogesh Simmhan

Website: ,

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 architecutures and distributed algorithms for distributed systems like cloud computing, edge computing, accelerators and quantum computing, with applications to emerging domains like drones/UAVs, AR/VR, hyperledger and smart city/IoT.

For the 2023 Admissions cycle, the DREAM:Lab is looking for 3-4 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
  • Platforms and algorithms for hybrid quantum computing and cloud computing
  • NoSQL and Big Data platforms for spatio-temporal and video data
  • Incremental and distributed algorithms for large streaming graphs in social netwoks 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. In search of an understandable consensus algorithm, Diego Ongaro and John Ousterhout, USENIX ATC, 2014,
  4. TiFL: A Tier-based Federated Learning System, Zheng Chai, et al, HPDC, 2020

Prior training: Students must have a Bachelors/Masters degree in Computer Science, Electrical, Electronics or Communications. Strong programming, algorithms and systems skills required. Ph.D. candidates preferred.

3. Middleware And Runtime Systems (MARS) Lab

Faculty: Sathish Vadhiyar


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:

  1. 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.

4. Natural Language Processing (NLP) Lab

Faculty: Danish Pruthi


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 plan to initiate projects related to inclusive model evaluation, model control (e.g., editing, de-biasing trained models) and also on 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


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


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 deep learning (zero-shot/few shot learning etc.), 2. Learning across modalities/domains (e.g., text-based image retrieval, sketch-guided localization, visual question answering, domain adaptation etc.) and 3. Video surveillance (e.g., person-reidentification, anomaly detection, human gait and pose analysis etc.)

Interview topics: Linear Algebra, Probability and Machine Learning basics.

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. Strong programming skills are expected.