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 .
Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .
Ishan Sharma is a Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), specializing in Mechanics and Applied Mathematics. His research focuses on granular materials, planetary science, contact mechanics and adhesion, soft materials, dynamics, structural vibrations, wave propagation, stability, and fluid-structure interaction. Dr. Sharma's research interests include modeling granular systems for geophysical and industrial applications, with specific emphasis on dynamics of granular minor planets and segregation in granular mixtures. His work bridges theoretical mechanics with practical applications in both space science and engineering contexts. The research spans multiple disciplines, connecting planetary science, materials science, and mechanical engineering through mathematical modeling and computational approaches. His scholarly contributions demonstrate significant trends in applying mechanical principles to celestial bodies and industrial processes. The work on granular materials has important implications for understanding asteroid formation, while his contact mechanics research informs material science and engineering design. His publications reveal a consistent focus on stability phenomena across different physical systems. INAE Young Engineer Award Dr. Sharma leads the Mechanics and Applied Mathematics research group at IIT Kanpur, supervising research projects that examine fundamental properties of materials under various mechanical conditions. His work combines theoretical analysis with computational methods to address complex mechanical problems with both academic and practical significance. He maintains an active research program with ongoing projects examining the mechanical behavior of granular systems in space environments. Based in office NL-102 in the Department of Mechanical Engineering, Dr. Sharma contributes significantly to the academic community through his teaching, research supervision, and scholarly publications in high-impact journals.
Aditya K. Jagannatham is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). With expertise in wireless communications and signal processing, he has established himself as a leading researcher in 5G/6G technologies, MIMO systems, and cognitive radio networks. His educational background includes: PhD in Electrical and Computer Engineering from UC San Diego (2007) M.S. in Electrical and Computer Engineering from UC San Diego (2004) B.Tech. in Electrical Engineering from IIT Bombay (2001) Professor Jagannatham's research primarily focuses on next-generation wireless communication systems, with special emphasis on 5G and 6G technologies. His work spans OTFS modulation, Terahertz communications, Visible Light Communication (VLC), Intelligent Reflecting Surface (IRS) technology, Massive MIMO, mmWave MIMO, Non-Orthogonal Multiple Access (NOMA), and Filter-Bank Multi-Carrier (FBMC) systems. His research integrates theoretical analysis with practical implementation challenges, addressing critical issues in modern wireless networks. His recent publications demonstrate a strong trend toward advanced signal processing techniques for next-generation wireless systems, particularly focusing on Sparse Bayesian Learning approaches for channel estimation, cooperative communication systems with energy harvesting capabilities, and millimeter wave MIMO technologies. His work bridges theoretical communication theory with practical implementation challenges in emerging wireless standards. Professor Jagannatham has received numerous prestigious awards and fellowships: Arun Kumar Endowed Chair Professorship (2019) Qualcomm Innovation Fellowship (2018) P.K. Kelkar Young Faculty Research Fellowship for excellence in research (2015-2018) IEEE Signal Processing Society travel grant to attend ICASSP 2015 Gopal Das Bhandari Memorial Distinguished Teacher Award (2012-13) Cal(IT)2 fellowship for graduate study at UC San Diego As an educator, Professor Jagannatham has received commendation letters from the Director of IIT Kanpur for excellence in teaching courses including EE624 Information and Coding Theory, EE670 Wireless Communications, and EE320 Principles of Communication Systems. His research has attracted significant funding, though specific grant details are not provided in the available information. He likely supervises graduate students working on cutting-edge wireless communication research. Professor Jagannatham is based in the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, where he leads research in wireless communications. His work appears to be closely connected with the Center for Developing Intelligent Systems (CDIS) and other research centers at IIT Kanpur focused on next-generation communication technologies.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
S. C. Srivastava is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur) and serves as the Director of the IIT Kanpur-La Trobe University Research Academy, highlighting his leadership in international academic collaboration. Dr. Srivastava earned his PhD in Electrical Engineering from IIT Delhi in 1987 and completed his B.Tech from Banaras Hindu University in 1976. His research spans power system dynamics & stability studies, optimal power dispatch and state estimation, security analysis and control, smart grid technologies, and renewable energy integration including DC and AC micro-grids. His publication record shows consistent research output through 2018 with significant contributions to power system state estimation, microgrid protection, and wide area control systems. His work demonstrates a clear progression toward addressing contemporary challenges in power systems, particularly renewable integration and smart grid applications. Malaviya Award for Excellence in Power Systems (2018) Prof GK Dubey Memorial IEEE UP Section Life Time Achievement Award (2016) The Life time Achievement Award for Academic Excellence (2016) Dr. Srivastava serves in influential advisory roles including Chairman of the Expert Committee on Mission Innovation in Smart Grids (DST, New Delhi) and Member of the R&D Advisory Committee of NETRA, NTPC. His leadership in the IIT Kanpur-La Trobe University Research Academy demonstrates ongoing commitment to addressing global energy challenges through international collaboration.
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.
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
Y. N. Singh is a Professor in the Department of Electrical Engineering at Indian Institute of Technology Kanpur . He holds a PhD and M.Tech from IIT Delhi and a B.Tech from REC Hamirpur . Education PhD, IIT Delhi (1997) M.Tech, IIT Delhi (1992) B.Tech, REC Hamirpur (1991) His research focuses on telecom networks , optical networks , complex networks , and wireless sensor networks , with significant contributions to network protection protocols, rumor propagation modeling, and optical memory design. His work on p-cycles has advanced telecom network survivability techniques. Selected publications include key papers in IEEE/OSA Journal of Lightwave Technology and Acta Physica Polonica B . He has received the AICTE Young Teacher Award (2002) and the Dr. M.N. Saha Memorial Award (2006). Contact details Office: EE/ACES, IIT Kanpur, UP, India-208016 Phone: 0512-[679/392/259]-7944 (O) Lab: 0512-[679/392/259]-7841, 7070 Fax: 0512-259-0063
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.
T. Ravichandran is a Professor in the Department of Humanities and Social Sciences at the Indian Institute of Technology Kanpur (IIT Kanpur) . His academic focus lies in Postmodern American Literature , Critical Theory , and Communication Skills . He has a PhD from Pondicherry University and has received prestigious recognitions including the Fulbright-Nehru Fellowship and a Gold Medal for Best PhD Thesis in English . Education: PhD, Pondicherry University (2000) MA, Madurai Kamaraj University (1988) BA, Madurai Kamaraj University (1986) His research spans postmodern narratives, cyberpunk, and the intersection of literature with technology and society. He has extensively analyzed the works of Thomas Pynchon and the cultural implications of postmodern science fiction. Scientific Awards & Honors: Fulbright-Nehru Academic and Professional Excellence Fellowship (2014–2015) Gold Medal for Best Ph.D. Thesis in English, Pondicherry University UGC–NET Lectureship Qualification Teacher Research Grant, ASRC, Hyderabad (twice) A. V. Tilak Prize for First in M.A. (1986–1988) He is actively involved in teaching and research, with a strong record of publications in peer-reviewed journals and conference proceedings. His work reflects a deep engagement with postmodern theory, American literature, and communication pedagogy.
Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.
Prof. M. P. Gururajan is a faculty member in the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay (IIT Bombay). His research is centered on computational approaches to understanding materials behavior, with a focus on phase transformations and microstructural evolution. Research Interests: Computational Materials Science Phase Transformations Microstructural Evolution Thermodynamics & Mechanics of Materials Phase Field Modelling His recent publications demonstrate expertise in simulating precipitate growth and grain boundary dynamics using phase field methods. These works, published in high-impact journals like Acta Materialia and Journal of Materials Science , reflect a strong trend in computational modeling of microstructure evolution under thermomechanical influences. Contact Information: Email: guru.mp@iitb.ac.in Blog: http://mogadalai.wordpress.com Prof. Gururajan has not listed any scientific awards or student advisees in the provided text. There is no mention of grants or external funding. He maintains an active research presence through publications and an academic blog.
Indian Institute of Technology Hyderabad (IITH)India
Shashank Vatedka is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad . His research focuses on information theory , coding theory , and their applications to data compression , statistical inference , and security . He holds a PhD from IISc, Bengaluru and has postdoctoral experience at Institut Polytechnique de Paris and The Chinese University of Hong Kong . Education : PhD and MSc (Engg) in Electrical Communication Engineering, IISc, Bengaluru (2011-17) Academic Positions : Assistant Professor, IIT Hyderabad (2019-present) Postdoctoral Fellow, Telecom Paris (2018-19) Research Assistant/Postdoctoral Fellow, Institute of Network Coding, CUHK (2016-18) His research spans three main areas: distributed inference (federated learning, wireless sensor networks), compression with locality constraints (local decoding, low-complexity algorithms), and communication in adversarial environments (jamming, list decoding). Recent work includes distributed mean estimation with limited communication and adversarial channel coding with partial information. He has received several honors including the Seshagiri Kaikini Medal for best PhD thesis at IISc in 2017, Best Paper Awards at NCC 2023 and Stanford Compression Workshop 2021, and the TCS Research Fellowship (2014-17). He serves as a Faculty Placement Coordinator at IIT Hyderabad and organizes international conference tracks. His research group advises students across PhD, MTech, BTech , and internships , with alumni pursuing advanced degrees at institutions like UCSD , Columbia University , and TU Delft . Collaborations include theoretical work with colleagues like Yihan Zhang and Sidharth Jaggi .
Rajesh M. Hegde is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. He holds a PhD in Computer Science from IIT Madras (2005), an M.E. in Electronics Engineering from Bangalore University (1988), and a B.E. in IT Engineering from Mysore University. His research focuses on Machine Learning , AI , and multimodal systems, with applications in wireless networks, IoT, and speech/audio processing. Specific interests include federated learning, WSN, and information fusion for ASR/VR systems. His lab is located in ACES 203-204. Publications predominantly explore signal processing techniques for multimedia and speech applications, showing consistent focus on feature extraction, multimodal fusion, and real-time system design across 15+ years of research. Awards & Honors: P.K Kelkar Research Fellowship (2009-2013) Undergraduate design mentorship award, UC San Diego ISCA Grant at INTERSPEECH-ICSLP 2004 IBM Best Thesis Award recommendation Teaching excellence commendation
