Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.
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.
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.
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
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.
Om P. Damani is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He serves as Faculty In-Charge of the Sustainable Development unit of the Center for Policy Studies and is also associated with the Centre for Technology Alternatives for Rural Areas (CTARA). His work bridges computer science with social development challenges, focusing on practical applications for rural communities. Dr. Damani's research interests span Technology for Development of the bottom 80%, System Dynamics: Modeling and Simulation for Social Development, System Architecture, and Data Science. His work demonstrates how computational approaches can address complex development challenges through projects like GramDrishti (for detecting rural infrastructure in satellite images), JalTantra (for optimizing water distribution networks), and FAI (Farm Assessment Index for holistic farming practice evaluation). His publications reveal a consistent focus on applying computer science to solve real-world problems in water management, agricultural systems, and rural infrastructure. His research has been recognized with significant awards including the IIT Bombay Industrial Impact Award 2010, IIT Bombay Impactful Research Award 2019, and Best Poster Award at Agriculture Science Congress 2017. Dr. Damani has successfully translated theoretical research into practical tools that address development challenges, particularly in water resource management and agricultural systems. As an educator, he has mentored numerous PhD students including Chintan Tundia, Shreenivas Kunte, Nikhil Hooda, Sivamuthu Prakash Murugan, Dipak L. Chaudhari, Prateek Kapadia, and Manoj K. Chinnakotla. His teaching portfolio includes courses on System Dynamics: Modeling and Simulation for Development (CS 752), Program Derivation (CS 420), and ICT for Development. Dr. Damani's educational background includes a Ph.D. in Computer Sciences from the University of Texas at Austin (1994-1999), B.Tech. in Computer Science and Engineering from IIT Kanpur (1990-1994), and prior professional experience at IBM T J Watson Research Lab and Akamai Technologies.
R.K. Shyamasundar is a Professor at the Indian Institute of Technology Bombay , with a focus on Real-Time and Reactive Programming, Logic Programming, Pi-Calculus, and Parallel Programs. Research spans formal verification, concurrency, and distributed systems. Key contributions include RT-CDL semantics, Esterel language extensions, and hybrid system controller synthesis. Scientific awards include JC Bose National Fellow, Fellowships at Indian Academy of Sciences and Indian National Science Academy, and Senior Membership in IEEE. His work involves collaborations with institutions like TCS Group and researchers such as Basant Rajan, N. Raja, and Deepak Kapur.
Gurunath Gurrala serves as an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Science (IISc), Bangalore. His research focuses on power systems dynamics, high-performance computing applications, and renewable energy integration. He maintains active collaborations with international institutions including Oak Ridge National Lab and Texas A&M University. His research interests center on Power Systems Analysis and Control , with specialization in High Performance Computing Applications, Nonlinear and Intelligent Control, Weak Grid Integration of Renewables, Microgrid Protection, and Smart Grid Stability. His work bridges theoretical control systems with practical power grid challenges, particularly for renewable-rich grids. His recent publications demonstrate a strong interdisciplinary trend, spanning power systems (35%), control theory (25%), renewable integration (20%), and emerging applications in biomedical engineering and environmental systems (20%). Key recurring themes include grid stability under high renewable penetration, advanced protection schemes for microgrids, and computational methods for power system analysis. IEEE Power and Energy Society (PES) Outstanding Engineer Award 2018 Young Engineer Award 2015, Indian National Academy Engineers Best Conference Paper, IEEE PES General Meeting 2015 Best Ph.D Thesis Award (Prof.D.J.Badkas Medal) 2010 Elevated to Senior Member IEEE (2016) Professor Gurrala has secured competitive research funding including the Young Scientist Grant from DST (2015) and International Travel Support from SERB (2017). He actively mentors students through PhD and Master's programs while teaching advanced courses including Power System Dynamics and Control (E4 231), Computer Control of Power Systems (E4 233), and Selected Topics in Integrated Power Systems (E4 237). His research group collaborates with power utilities and international research labs on grid modernization challenges.
Karthik P. N. is an Assistant Professor in the Department of Artificial Intelligence at the Indian Institute of Technology Hyderabad (IIT Hyderabad). He previously served as a Research Fellow at the Institute of Data Science, National University of Singapore (NUS), and completed his Ph.D. and Master of Science (Engineering) at the Department of Electrical Communication Engineering, Indian Institute of Science (IISc), Bengaluru, under the guidance of Prof. Rajesh Sundaresan. Earlier, he worked as a Project Assistant in Prof. Chandra R. Murthy's lab at IISc and earned a Bachelor of Engineering in Electronics and Communications from R V College of Engineering, Bengaluru. Ph.D., Electrical Communication Engineering, IISc Bengaluru M.Sc. (Engineering), IISc Bengaluru B.E., Electronics and Communications, R V College of Engineering His research focuses on Probability Theory, Detection and Estimation Theory, Markov Decision Processes , and Multi-armed Bandits , with applications in Federated Learning, Differential Privacy, Reinforcement Learning, Information Theory , and Statistics . Recent work explores optimal strategies for best arm identification in restless bandits and privacy-preserving machine learning. He has received high instructor ratings for courses like Stochastic Processes and Programming for AI , and was honored with the Faculty Teaching Excellence Award 2025 at IIT Hyderabad. His Google Scholar articles highlight advances in 3D point cloud security, railway accident prevention, and vision-language model robustness . Scientific awards include the Faculty Teaching Excellence Award 2025 and First place in the INAE Kanpur Chapter 100 Seconds Competition (2021) . He collaborates with researchers like Prof. Vincent Y. F. Tan and Prof. Yeow Meng Chee, and has served on technical program committees for conferences such as APWDSIT 2025 and ISIT 2025 . Teaching roles at IIT Hyderabad involve graduate-level courses on Probability and Stochastic Processes , cross-listed with Electrical Engineering.
Dr. Preethi Srivathsa is an Assistant Professor - Senior Scale in the School of Computer Engineering at Manipal Academy of Higher Education (MAHE), Bengaluru. She holds a B.Tech, M.Tech, and Ph.D. (awarded by Presidency University in 2022). Her academic career includes positions at Presidency University (2019-2023) and East Point College of Engineering (2008-2019). Her research focuses on: Computer architecture and low-power hardware design IoT applications and cyber-physical systems Cryptography and blockchain security Machine learning implementations in hardware FPGA-based accelerators and optimization techniques Her publication portfolio shows strong emphasis on hardware-efficient algorithms, cryptographic systems (especially elliptic curve applications in blockchain), and emerging IoT architectures. Recent work integrates machine learning with hardware acceleration for smart home systems and agricultural technology. Awards and recognitions: Best Paper Award at IEEE iSES-2021 for low-power sorter design Infosys Bronze Partner Faculty (2013) She has developed intellectual property including IoT-based monitoring systems and blockchain educational frameworks. Technical skills include Verilog, FPGA design, IoT platforms (Arduino/Raspberry Pi), and multiple programming languages.
Dhananjay Dhamdhere is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay (IIT Bombay). He has maintained an active research and teaching career since at least the 1980s, with current involvement in course instruction and academic initiatives. Research Interests: Optimizing Compilers and Partial Redundancy Elimination Operating Systems and Distributed Systems Detection of Data Races and Atomicity Violations Safety issues in Dynamic Software Updating Debugging of Optimized Programs His research demonstrates a consistent focus on practical compiler optimizations, evolving from foundational work in data flow analysis to contemporary challenges in dynamic software updating. The publications show particular expertise in making theoretical compiler concepts implementable in real-world systems, with significant contributions to partial redundancy elimination techniques. Research Funding: Department of Electronics research grant on Optimizing Compilers (1985) Grants from Tandem Computers (1995), Intel (1998), Microsoft Research (2001, 2002), and Sun Microsystems (2001, 2002) Professor Dhamdhere has successfully transferred technology to industry through his Gensat system, a generator for static analysis tools. He actively contributes to academic culture improvement through the Forum for Academic Culture (FAC), which he co-founded in 2013 to address student motivation, teaching methodologies, and large class challenges at IIT Bombay.
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.
Rushikesh K. Joshi is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Bombay. His research focuses on computational models for programs, their structures and dynamics, including processes, interactions, concurrency, and distributed systems. He has taught advanced courses like CS 770 (Process Engineering), CS 787 (Language Engineering), and CS 757 (Design and Re-engineering). Research Interests: He specializes in software engineering, distributed systems, program analysis, and code refactoring. His work includes modular process modeling, trace language mining, and consistency models for business processes. He also explores objectification of procedural code through projects like Ox and AccessViz. Notable Projects: Anonymous Remote Computing (ARC), Filter Objects, Constore Graph Database, and Ox (objectification of Linux kernel structures). Funding sources include IBM, Microsoft, Infosys, and the Ministry of Information Technology. Scientific Contributions: His research has led to publications in IEEE Transactions, LNCS, and other venues. He has advised over 100 theses, including Ph.D. students Karnika Shivhare, Omkarendra Tiwari, and Vrinda Yadav.
Supratim Biswas is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay, where he has served since 1995. His academic career spans over four decades, beginning as a Lecturer in the Computer Center in 1980, progressing to Assistant Professor in 1985, Associate Professor in 1990, and achieving full Professorship in 1995. He has held significant administrative roles including Dean of Academic Programs (2007-2010), Head of CSE Department (2000-2003), and Director of IITB-Monash Academy (2009-2010). His research interests focus on Programming Languages, Compiler Optimization, Parallelizing Compilers, Parallel and Distributed computing, and Combinatorial Optimization . Professor Biswas has made substantial contributions to compiler technology, particularly in parallelization techniques for modern architectures. His work bridges theoretical compiler design with practical applications in high-performance computing and CAD systems, demonstrating how compiler optimizations can significantly enhance computational efficiency in real-world applications. The publication record shows a consistent research trajectory spanning nearly four decades, with recent work (2012-2015) focusing on GPU-based parallel algorithms, loop parallelization techniques for non-uniform data dependencies, and mesh processing for CAD applications. His research demonstrates evolution from foundational compiler theory to contemporary parallel architectures, maintaining relevance through practical applications in computational geometry, CAD systems, and high-performance computing. Excellence in Teaching Award (2000) Professor Biswas has supervised over 60 doctoral and master's students, establishing himself as a dedicated mentor in systems software education. His sponsored research portfolio includes significant projects with CDAC (350 lacs), MIT (133 lacs), TCS (81.3 lacs), and Intel Corporation (10 lacs), demonstrating strong industry-academic collaboration. His teaching portfolio spans both undergraduate and postgraduate levels, including foundational courses like Discrete Structures and advanced topics like Parallelizing Compilers, reflecting his commitment to curriculum development across multiple generations of computer science education. His laboratory work has supported students across B.Tech, M.Tech, and Ph.D. programs, with particular emphasis on compiler construction and operating systems. Through the Continuing Education Program, he has extended his expertise to industry professionals, conducting numerous specialized courses for organizations including VSNL, TCS, DRDO, and Reliance.
Ashish Mishra is an Assistant Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad) , located in Kandi, Sangareddy, Telangana, India. Education: Ph.D. from Indian Institute of Science (IISc), Bengaluru Research Interests: His research spans foundational and applied areas in computer science, focusing on: Program Verification – ensuring correctness of software systems through formal methods. Program Synthesis – automatically generating programs from high-level specifications. Programming Languages – design, semantics, and implementation of new language constructs. Neurosymbolic Programming – integrating neural networks with symbolic reasoning for smarter program synthesis and verification. Current Affiliation: Room CS-606, Computer Science Building, IIT Hyderabad Contact: No public email provided; contact form available on institute page.
