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Shivaram KalyanakrishnanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +5 more

Shivaram Kalyanakrishnan is an Associate Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay , specialising in Artificial Intelligence and Machine Learning . His research spans sequential decision making , multiagent learning , multi-armed bandits , and humanoid robotics , with applications in robot soccer , computer games , and online advertising . He teaches advanced courses like CS 747: Foundations of Intelligent and Learning Agents and CS 748: Advances in Intelligent and Learning Agents , focusing on end-to-end system design and theoretical analysis. His scientific awards include the Best Student Paper Award at RoboCup International Symposium 2006 and nomination for Best Student Paper Award at AAMAS 2007 . His work on reinforcement learning and policy iteration has been published in leading venues such as IJCAI , ICML , and COLT , with recent contributions to railway scheduling and bandit algorithms. While no explicit list of advisees is provided, his research projects and publications suggest mentorship of students in collaborative efforts. Contact : shivaram@cse.iitb.ac.in .

Supratik ChakrabortyView profile

Professor

• Formal Methods
• Formal Verification
• Automated Synthesis
• +7 more

Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.

Arpit AgarwalView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Human-AI Interaction
• +4 more

Arpit Agarwal is an Assistant Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology, Bombay. He previously held postdoctoral positions at FAIR Labs (Meta) working with Max Nickel and at the Data Science Institute at Columbia University hosted by Prof. Yash Kanoria and Prof. Tim Roughgarden. He completed his PhD from the Department of Computer & Information Science at the University of Pennsylvania under the guidance of Prof. Shivani Agarwal. His research focuses on the intersection of human behavior and machine learning systems, with particular interest in learning from implicit, strategic, and heterogeneous human feedback. His work spans multiple dimensions of human-AI interaction including understanding long-term dynamics between humans and AI systems, designing responsible AI, and studying misalignment between user preferences and system objectives. His research methodology often combines theoretical machine learning with practical applications in recommendation systems and social AI. His recent publications reveal a strong focus on bandit algorithms, preference learning, and recommendation systems, with increasing attention to responsible AI design and human-centered considerations. His work demonstrates expertise in theoretical machine learning with applications to real-world problems, particularly in understanding how humans interact with and are influenced by AI systems over time. Dr. Agarwal teaches advanced courses including CS767 Theoretical Machine Learning (Autumn 2025) and CS6103 Human-Centered AI: From Learning Models to Responsible Systems (Spring 2025), which covers topics such as AI alignment, learning from pairwise comparisons, crowdsourcing, human-in-the-loop decision making, recommendation systems, interpretability, privacy, fairness, causality, and AI governance.

• Databases
• Data Mining
• Information Retrieval
• +2 more

Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650

Krishna JagannathanView profile

Professor

• Stochastic modeling
• Communication networks
• Information theory
• +3 more

Krishna Jagannathan is a full-time Professor in the Department of Electrical Engineering at the Indian Institute of Technology Madras (IIT Madras), India. He specializes in stochastic modeling, communication networks, information theory, and queuing theory. He obtained his B.Tech from IIT Madras in 2004, followed by S.M. and Ph.D. degrees from MIT in 2006 and 2010, respectively. After post-doctoral positions at Caltech and MIT, he joined IIT Madras in 2011. Education: B.Tech in Electrical Engineering, IIT Madras (2004) S.M. in Electrical Engineering and Computer Science, MIT (2006) Ph.D. in Electrical Engineering and Computer Science, MIT (2010) Research Interests: His research focuses on stochastic modeling and analysis of communication networks , information theory , and queuing theory . He has made significant contributions to understanding network performance, resource allocation, and risk-aware decision-making in complex systems. He leads the Networks and Stochastic Systems lab at IIT Madras, mentoring a large cohort of Ph.D. and M.S. students working on cutting-edge problems in networking, optimization, and stochastic systems. Scientific Awards: Best Paper Award at WiOpt 2013, Tsukuba, Japan Young Faculty Recognition Award for Excellence in Teaching and Research, IIT Madras (2014) Teaching & Mentorship: He has taught a wide range of courses including Probability Foundations , Stochastic Modeling and Queuing Theory , Convex Optimization , and Signals & Systems , consistently receiving high teaching evaluations. He has supervised over 15 Ph.D. and M.S. students to completion and continues to guide several active researchers.

• Computational tomographic reconstructions
• subsurface imaging
• shape/target tracking
• +8 more

Naren Naik is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology (IIT) Kanpur, specializing in computational tomographic reconstructions and analysis for subsurface imaging and shape/target tracking. His educational background includes: PhD from the Indian Institute of Science (IISc) Bangalore in 2000 M.E. in Electronics and Communication Engineering from IISc Bangalore in 1992 B.Sc. from Bangalore in 1988 Professor Naik's research focuses on development and analysis of reconstruction algorithms for nonlinear tomography , with particular emphasis on shape-based and dynamic tomography, tracking and battlefield surveillance, and numerical solutions to partial differential equations in electromagnetics. His work spans multiple imaging modalities including subsurface imaging with Ground Penetrating Radar (GPR), fluorescence optics, electrical impedance tomography, and photoacoustic tomography. His research bridges theoretical mathematics with practical applications in electromagnetic imaging and target tracking systems, addressing complex inverse problems in computational imaging. His publication record shows a clear progression from electromagnetic tomography to advanced Kalman filtering techniques for target tracking applications. The most recent works focus on wireless sensor networks and maneuvering target tracking, demonstrating his ability to adapt theoretical frameworks to evolving technological contexts while maintaining mathematical rigor in solving inverse problems. His professional recognition includes: Invited presentation at the special session on advances in model based inversion at the 2011 IEEE AP-S International Symposium on Antennas and Propagation Professor Naik maintains an active research program with consistent publication output in high-impact journals and conferences. His work demonstrates strong interdisciplinary collaboration, particularly with researchers in electromagnetics, signal processing, and imaging sciences. His research has significant applications in defense technology (battlefield surveillance), medical imaging, and subsurface exploration systems, contributing to both theoretical advances and practical implementations in these fields. He is based in Office 303A ACES (Advanced Centre for Electronic Systems) at the Department of Electrical Engineering, IIT Kanpur, where he leads research activities in computational imaging and tomographic reconstruction.

Rameshwar PratapView profile

Associate Professor

• Algorithms in Machine Learning
• Theoretical Computer Science
• Sketching and Dimensionality Reduction
• +5 more

Rameshwar Pratap is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Previously, he served as an Assistant Professor at the School of Computing and Electrical Engineering, IIT Mandi for three years. Education: Ph.D. in Theoretical Computer Science, Chennai Mathematical Institute Research Interests: His research lies at the intersection of theory and practice, focusing on extremely simple yet practical approximation algorithms with provable guarantees . Key themes include: Sketching and dimensionality reduction algorithms for tensors and similarity measures Improving speed, scalability, and accuracy of existing sketching methods Applications in machine learning: node embedding in large-scale networks, itemset mining, model compression He extensively employs techniques from matrix and tensor algebra, sampling, random projection, and randomized hashing . Publications Overview: Across 2021–2025 his work has appeared in top venues such as IEEE Globecom, Theoretical Computer Science, Acta Informatica, Information Processing Letters, Algorithmica, UAI, ICALP, Machine Learning, TKDE, and ACML. Recurring themes are randomized sketching, locality-sensitive hashing, compressed matrix multiplication, variance reduction, and subspace approximation , demonstrating both theoretical depth and practical impact. Awards & Honors: Early Career Research Grant (PM-ECRG) 2025, Anusandhan National Research Foundation (ANRF) Best Paper Award, COCOON 2020 Students & Funding: First Ph.D. student: Bhisham Dev Verma (co-advised with Prof. Manoj Thakur) graduated June 2025 MS by Research student: Punit Pankaj Dubey graduated October 2022 Currently hiring 1 Junior Research Fellow for the PM-ECRG project “Improving Similarity Search in Practice” Labs & Teams: Works within the Algorithms & Theory group at IIT Hyderabad, collaborating with national and international researchers. Erdös number is 3.

Mathukumalli VidyasagarView profile

Professor

• Machine Learning
• Systems and Control Theory
• Computational Biology
• +4 more

Mathukumalli Vidyasagar is a Distinguished Professor at the Indian Institute of Technology Hyderabad and previously held the SERB National Science Chair and Cecil & Ida Green Chair in Systems Biology Science at the University of Texas at Dallas. He earned his Ph.D. from the University of Wisconsin, Madison, and has authored 13 books and over 160 peer-reviewed papers.

Arijit BishnuView profile

Associate Professor

• Theoretical Computer Science
• Randomized Algorithms
• Approximation Algorithms
• +6 more

Arijit Bishnu is an Associate Professor at the Indian Statistical Institute in the Advanced Computing and Microelectronics Unit (ACMU). He has taught courses such as Design and Analysis of Algorithms , Randomized Algorithms , Computational Geometry , and Algorithms for Big Data over multiple years (2008–2025), focusing on theoretical and applied aspects of computer science. Research Interests: His work spans Theoretical Computer Science , Randomized and Approximation Algorithms , Computational Geometry , and Combinatorics . He explores problems in sublinear algorithms, streaming computation, geometric data analysis, and complexity theory. Publications: Recent papers include contributions to STOC 2025 , RANDOM 2025 , and APPROX 2024 , covering topics like property testing, triangle counting complexity, and streaming algorithms. Collaborations with researchers like Sourav Chakraborty, Gopinath Mishra, and Sayantan Sen highlight his interdisciplinary approach. Academic Leadership: He has co-organized research courses such as Approximation Algorithms and Topics in Algorithms and Complexity , emphasizing mentorship and knowledge dissemination in theoretical computer science. His comprehensive work integrates algorithmic innovation with rigorous mathematical analysis, advancing computational techniques for large-scale and geometric data.

Aravind N.R.View profile

Associate Professor

• Algorithms
• Graph Theory
• Combinatorics
• +5 more

Dr. N.R. Aravind is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. Holding a Ph.D. in Theoretical Computer Science from The Institute of Mathematical Sciences, Chennai, he is affiliated with the College of Engineering. His research spans algorithms, graph theory, combinatorics, and determinantal complexity, with a focus on parameterized algorithms and approximation techniques for NP-hard problems. Education: Ph.D., The Institute of Mathematical Sciences, Chennai (Advisor: Prof. C.R. Subramanian) Postdoctoral research under Prof. Sundar Vishwanathan at IIT Bombay Aravind's research explores structural questions in graph theory, social network modeling, and complexity bounds. His work on parameterized algorithms addresses tractable instances via input parameters, while his studies in graph coloring and forbidden subgraphs contribute to theoretical understanding. Recent publications focus on matching cut problems, happy coloring, and determinantal complexity. His 15 most recent publications (2010-2024) span computational complexity, graph algorithms, coloring problems, and determinantal complexity. These works emphasize parameterized approaches, structural graph theory, and algorithmic bounds for intractable problems. Scientific Awards: Aravind has mentored numerous PhD and MTech students, including Roopam Saxena, Anjeneya Swami Kare, and R.B. Sandeep, now holding academic positions at prestigious institutions. He has taught courses like Algorithms, Probability in Computing, and Cryptology, often collaborating with co-instructors such as Dr. Rakesh Venkat. His office is located in Room CS-408, Indian Institute of Technology Hyderabad.

Jyothi VeduradaView profile

Assistant Professor

• Compilers
• Program Analysis
• High-Performance Computing
• +3 more

Jyothi Vedurada is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Her research focuses on compilers, program analysis, high-performance computing (HPC), and software engineering. Prior to joining IIT Hyderabad, she was a postdoctoral researcher at Microsoft Research Lab in Bangalore and worked as a Software Engineer at Hewlett Packard in Chennai. Her research spans four major areas: Compiler Optimizations : Developing global compiler optimizations for CPU-GPU heterogeneous systems to enhance performance across varied hardware environments. ML for PL : Leveraging AI to overcome scalability and semantic challenges in program analysis, with applications in bug detection, API misuse recommendation, and algorithm description generation for Jupyter notebooks. Parallelization : Designing efficient GPU algorithms for tasks like Approximate Nearest Neighbour search and subgraph isomorphism optimization, alongside developing the Tensor Transposition Library for GPUs (TTLG). Concurrency Testing : Creating systematic testing frameworks for concurrent systems, including language-agnostic libraries and mock storage systems to validate weak isolation levels in databases. Scientific Awards & Recognition : Distinguished Artifact Award at ECOOP 2025 Google Research exploreCSR Award (2021) Tata Consultancy Services PhD Fellowship ACM SIGAI, SIGPLAN, and IEEE Travel Grants Advising & Collaborations : Supervising 11 PhD and MTech students, including Soumik Kumar Basu and Karthik V, with projects spanning compiler optimizations, HPC, and AI-driven software analysis. Collaborating with institutions like Microsoft Research and IIT Madras.

L. Sunil ChandranView profile

Professor

• Graph Theory
• Combinatorics
• Algorithms
• +5 more

L. Sunil Chandran is a Professor at the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore. His research spans graph theory, combinatorics, algorithms, and quantum computing, with extensive work on graph coloring, boxicity, rainbow connectivity, and computational complexity. He leads a large research group, advising PhD, MSc, and ME students, many of whom hold faculty positions at premier institutions like IITs, NITs, and international universities. Chandran's research focuses on combinatorial graph algorithms, structural graph theory, and applications in quantum physics. Key areas include approximation algorithms for NP-hard problems, graph representation (boxicity/cubicity), and rainbow coloring variants. His recent work explores quantum entanglement via hypergraphs and parameterized complexity for vertex deletion problems. His publications (15 most recent shown) demonstrate a consistent focus on graph-theoretic problem-solving, with innovations in algorithmic techniques for graph coloring, combinatorial optimization, and quantum-graph intersections. Trends include increased interdisciplinary work bridging computer science, physics, and discrete mathematics. Awards and Recognitions: Adjunct Professorship at NIT Calicut (2023) INSA Fellow (2022) & INAE Fellow (2015) Alexander von Humboldt Fellowship NASI-SCOPUS Young Scientist Award (2012) MSR India Outstanding Young Faculty Award (2009-2010) INSA Young Scientist Award (2007) He has secured grants from DST, UGC, SERB, and NBHM to support postdoctoral researchers and students. Current projects include quantum-graph theory applications and parameterized algorithms. His group maintains active collaborations with institutions globally.

K R GuruprasadView profile

Associate Professor

• Robotics and Autonomous Systems
• Motion Planning
• Multi-Robot Systems
• +7 more

K R Guruprasad is an Associate Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). With a PhD in Aerospace Engineering from the Indian Institute of Science (IISc) Bangalore, he has established himself as a leading researcher in robotics, motion planning, and multi-robot systems. His research interests span a wide range of topics in robotics including Robotics-Mechanics and Control, Motion Planning, Multi Robots Systems, Cooperative Control, Exo-Skeleton Control, and Voronoi Partition applications. His work particularly focuses on UAV/Drones motion planning, mobile manipulation, and artificial intelligence applications in robotics. His research integrates theoretical foundations with practical implementations, evidenced by his numerous publications in high-impact journals. Prof. Guruprasad's scholarly contributions include a book titled 'Robotics: Mechanics and Control' (PHI, 2019) and several edited volumes. His publication record shows consistent output with an h-index of 11, i10 index of 14, and 382 citations according to Scopus data. His recent work demonstrates strong focus on multi-robot coverage algorithms, UAV applications, and distributed control systems. Among his recognitions are a best presentation award at the IISc-IBM Research Workshop (2008) and a best paper award finalist at DARS 2014. His research has practical applications in search and rescue operations, landmine detection, and autonomous exploration. Prof. Guruprasad maintains active collaborations with researchers globally and has contributed significantly to advancing robotics research in India through his work at IIT Kanpur's Mechanical Engineering department.

• Machine Learning
• Bayesian Statistics
• Latent Variable Models
• +6 more

Piyush Rai is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He also holds an Adjunct Assistant Professor position in Electrical and Computer Engineering at Duke University. His academic journey includes postdoctoral research at Duke University and the University of Texas at Austin, following his PhD from the University of Utah. His research interests focus on Machine Learning and Bayesian Statistics, with specializations in Latent Variable Models, Probabilistic Modeling, Approximate Inference, and Nonparametric Bayesian Methods. His work bridges theoretical foundations with practical applications in artificial intelligence and data science. Dr. Rai's publication record shows a consistent focus on tensor factorization, Bayesian nonparametrics, and scalable algorithms for large datasets. His work spans conferences including NIPS, ICML, UAI, and AISTATS, demonstrating strong contributions to both theoretical and applied machine learning. Best Student Paper Award at ECML-PKDD (2015) National Science Foundation (USA) EAGER Award (2015) Dr. Deep Singh and Daljeet Kaur Faculty Fellowship at IIT Kanpur (2015) NIPS 2013 Reviewer Award Sheldon Ekland-Olson Postdoctoral Fellowship (2012) He teaches advanced courses in Machine Learning and Probabilistic Machine Learning at IIT Kanpur, mentoring the next generation of researchers in statistical machine learning techniques. His collaborative work with Lawrence Carin and other researchers demonstrates strong interdisciplinary connections between institutions.

Palash DeyView profile

Assistant Professor

• Theoretical Computer Science
• Parameterized Algorithms
• Approximation Algorithms
• +7 more

Palash Dey is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur. His research lies at the intersection of theoretical computer science and algorithmic game theory, with a primary focus on computational social choice, voting theory, and parameterized algorithms. His research interests include: Theoretical Computer Science Parameterized Algorithms Approximation Algorithms Algorithmic Game Theory Computational Social Choice Voting Theory Algorithmic Fairness Network Games His recent publications, spanning from 2023 to 2025, demonstrate a strong and consistent research trajectory in the analysis of voting systems, manipulation, and fairness. The articles focus on complex problems such as bribery, gerrymandering, rank aggregation, and networked public goods, primarily using tools from parameterized complexity and algorithmic game theory. Key venues for his work include AAMAS, IJCAI, AAAI, and Theoretical Computer Science, indicating a high impact in the fields of AI and theoretical computer science. His professional service includes being the Newsletter and Social Media Chair of the IEEE Kharagpur Section and serving on the Senior Program Committee for AAAI (2021-2024) and the Program Committee for AAAI, IJCAI, AAMAS, and COMSOC. He has also organized significant workshops such as CALDAM 2019 and GAME-ARTS. Palash Dey actively advises Ph.D. students, including Sipra Singh, Koustav De, Ashlesha Hota, and Narayan Sharma. He teaches courses such as Algorithms II, Randomized Algorithm Design, and Algorithmic Game Theory. His email is palash.dey@cse.iitkgp.ac.in.