Dr. Balaraman Ravindran is a Professor and Head of the Department of Data Science and Artificial Intelligence (DSAI) at IIT Madras. He also leads the Robert Bosch Centre for Data Science & Artificial Intelligence (RBCDSAI) and the Centre for Responsible AI (CeRAI). His research focuses on reinforcement learning, geometric deep learning, and ethical AI deployment. Education includes a PhD from the University of Massachusetts Amherst (2004) and MSc from the Indian Institute of Science, Bangalore (1996). He holds prestigious fellowships from AAAI and INAE, and is an ACM Distinguished Member. Key contributions include work on class imbalance learning (e.g., TODUS algorithm) and applications in healthcare, transportation, and social networks. He has advised over 20 students and secured grants from Google, TCS Research, and others. Labs/Teams: Heads RBCDSAI and CeRAI, collaborates with TCS Research and Google.
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 .
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.
Indian Institute of Technology Hyderabad (IITH)India
Antony Franklin is an Associate Professor and Head of the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IITH), India. He leads the Networked Wireless Systems (NeWS) Lab and is actively involved in 5G research, wireless networks, and cybersecurity. He earned his Ph.D. from IIT Madras in 2010. Education: Ph.D. in Computer Science and Engineering, IIT Madras, 2010 Research Interests: His research spans wireless networks , 5G systems , mobile edge computing , low-latency transport protocols , and cybersecurity . He focuses on next-generation mobile systems, especially 5G, aiming to meet the demands of ultra-low latency, high data rates, and quality of experience for services like IoT, autonomous driving, and VR. He has been involved in developing testbeds and prototypes to validate real-world performance of 5G technologies, including network slicing, LTE-WiFi integration, and edge computing. Scientific Awards: Best Academic Demo Award, IEEE COMSNETS 2018 Second Best Paper Award, IEEE ANTS 2017 Grants & Projects: Network Slice Life-cycle Management for 5G Mobile Networks – SPARC, MHRD (2019–2021) DNS/IPv6 for IoT Security – NASSCOM (2018–2019) CCRAN: Energy Efficiency in Cloud RAN – Intel India (2018–2021) End-to-End 5G Test-Bed – DoT, GoI (2018–2021) M2SMART Smart Cities – SATREPS (2018–2023) Ultra-Reliable Low Latency Protocols – STINT, Sweden (2017–2018) Low Latency 5G Protocols – SERB ECR (2016–2019) Lab & Team: He leads the NeWS Lab (Networked Wireless Systems) at IITH, which focuses on 5G, wireless networks, and IoT systems. The lab is actively involved in developing real-world testbeds and publishing in top-tier conferences and journals.
Manjesh Kumar Hanawal is an Associate Professor at the Industrial Engineering and Operations Research (IEOR) center of IIT Bombay , India. His academic journey includes a Ph.D. from University of Avignon/INRIA (2013), M.Sc (Engg) from IISc Bangalore (2009), and B.E. from NIT Bhopal (2004). Pre-Ph.D. work: Scientist-B at DRDO's CAIR Postdoctoral: Boston University (2013-2015) Appointed as first Professor-In-Charge of TCA2I center Research focuses on Machine Learning algorithms for limited feedback environments, Communication Networks resource allocation, and Cybersecurity threat detection. Publications span top venues like IEEE Transactions, NeurIPS, INFOCOM, and AISTATS. Recent work trends include: Bandit algorithms for distributed learning in heterogeneous networks Contextual information integration in sequential selection Energy efficiency optimization in wireless sensor networks Net neutrality violation detection frameworks Anti-jamming countermeasures in cognitive networks Scientific recognition includes the SERB Early Career Research Award (2019-2022) for machine learning applications in wireless networks. Advisees include Ph.D. awardee Arun Verma and Best Masters Thesis Awardee Sayan Chatterjee.
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 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.
Indian Institute of Technology Hyderabad (IITH)India
Aditya T Siripuram is an Associate Professor at the Indian Institute of Technology Hyderabad (IITH), holding joint appointments in the Department of Electrical Engineering and the Department of Artificial Intelligence. He completed his PhD at Stanford University and holds B.Tech and M.Tech degrees from IIT Bombay. Education: PhD in Electrical Engineering, Stanford University (2017) - GPA: 4.17/4 M.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 B.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 Research Interests: His research spans Fourier analysis, signal processing, machine learning, convex and combinatorial optimization, with applications in AI/ML and applied mathematics. His work particularly focuses on computational aspects of Fourier analysis, including fast DFT computation for structured signals, convolution idempotents, and graph-based signal processing techniques. His recent research directions involve developing efficient algorithms for computing Discrete Fourier Transforms for signals with structured frequency support, investigating relationships between additive structures in frequency domains and computational complexity, and exploring graph learning techniques under spectral constraints. Awards and Recognition: Excellence in Teaching Award, IIT Hyderabad (2019, 2022) Stanford Graduate Fellowship Qualcomm Innovation Fellowship (awarded to his PhD student Charantej Reddy P in 2021) Teaching and Service: He has taught courses including AI1110 Probability and Stochastic Processes, EE5609 Matrix Theory, EE5606 Convex Optimization, and EE5328 Introduction to Submodular Functions. He serves as Departmental Undergraduate Committee Chair for the Department of AI at IITH (2020-present) and was MTech Admissions Coordinator for the same department (2019-2022). Research Group: He currently advises three PhD students working on signal processing based graph learning techniques, DFT computation for structured signals, and coded computing problems.
Indian Institute of Technology Hyderabad (IITH)India
Kishalay Mitra is a Professor at the Indian Institute of Technology Hyderabad , with affiliations to the Department of Chemical Engineering , Department of Climate Change , and Department of Artificial Intelligence . He also holds visiting professorships at Washington University in St. Louis and University of Washington, Seattle . His work in the Global Optimization & Knowledge Unearthing Laboratory (GOKUL) spans interdisciplinary optimization, machine learning, and their applications in industrial-scale engineering problems. Education : Ph.D. from IIT Bombay. Research Interests : Mitra's research focuses on optimization under uncertainty , surrogate modeling , multi-objective optimization , and integrating machine learning with physics-based models . His work addresses real-world challenges in wind energy , bioenergy supply chains , chemical process control , nanoscience , and environmental modeling (e.g., PM10 spatiotemporal analysis, forest fire prediction, and carbon capture). Article Trends : His recent publications emphasize wind energy systems (layout optimization, yaw control, forecasting), materials science (precipitate growth prediction, polymerization), and industrial processes (crystallization, grinding circuits). Techniques include neural operators , Bayesian optimization , generative adversarial networks (GANs) , and explainable AI .
Dr. Prabodh Bajpai is a Professor in the Department of Sustainable Energy Engineering at the Indian Institute of Technology Kanpur. Previously, he served as an Associate Professor at the same department from July 2022 to December 2022, and before that at the Electrical Engineering Department of IIT Kharagpur from July 2014 to June 2022. He began his academic career as an Assistant Professor at IIT Kharagpur from June 2008 to July 2014. His educational background includes a Ph.D. in Electrical Engineering (Power Systems) from IIT Kanpur (2008), M.Tech in Energy Studies from IIT Delhi (2001), and B.E. in Electrical Engineering from IIT Roorkee (1997). Dr. Bajpai's research spans renewable energy integration, power system operation and control, microgrid technologies, and smart grid applications. His work focuses on practical implementation of sustainable energy solutions with emphasis on power electronics, energy storage, and grid stability. He has made significant contributions to the fields of distributed generation integration, protection schemes for renewable-rich systems, and energy management in microgrids. His recent publications show a strong focus on DC microgrids, multi-port power converters, and advanced control strategies for renewable integration. The research demonstrates increasing sophistication in power electronics applications for sustainable energy systems, with particular emphasis on practical implementations for commercial and agricultural applications. Dr. Bajpai actively mentors graduate students, currently supervising seven students across PhD and M.Tech programs. His teaching portfolio includes core courses in electrical power engineering, renewables-integrated smart power systems, and energy systems modeling and analysis at IIT Kanpur, building on his extensive teaching experience at IIT Kharagpur where he developed several new courses in renewable energy systems. His research group maintains a Hybrid AC/DC Microgrid test facility and has developed a Renewable Hybrid Energy Power Plant for stand-alone applications, demonstrating his commitment to translating theoretical research into practical implementations.
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.
