Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.
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 .
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.
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.
R K Bansal is a Professor in the Department of Electrical Engineering at Indian Institute of Technology Kanpur (IIT Kanpur). He has been actively contributing to the fields of Detection Theory and Information Theory for several decades with a strong academic background including a PhD from University of Connecticut (1987). His research interests include: Universal data compression with applications Sequential detection of a change in distribution Robust detection Ergodic theory and large deviation theory applications Stochastic processes Dr. Bansal's recent publications primarily focus on data compression algorithms, particularly variations of the Lempel-Ziv algorithm, and detection theory applications. His work demonstrates a strong theoretical foundation with practical applications in information processing and analysis, showing consistent research activity from the 1980s through 2013. He has received significant recognition for his teaching excellence: Letter of commendation from Director on best teaching (2011) Outstanding tutor for Mathematical Statistics (BSO209) based on student feedback (1998-II) Consistently high teaching evaluations exceeding institute averages Dr. Bansal has advised students and maintained active research in his specialized areas, though specific names of current advisees are not mentioned in the available information. His research has been published in prestigious venues including IEEE Transactions on Information Theory. His laboratory is located in room 202 ACES (Advanced Centre for Electronic Systems) at IIT Kanpur, serving as the base for his research activities in detection theory and information theory applications.
Subhajit Dutta is an Associate Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. He has established himself as a notable researcher in specialized statistical methodologies with publications in top-tier statistical journals. Dr. Dutta completed his PhD in Statistics from the Indian Statistical Institute (ISI), Kolkata in 2013 under the supervision of Prof. Probal Chaudhuri. His academic journey includes an M.Sc. in Statistics from IIT Kanpur (2007) and a B.Sc. in Statistics from Presidency College, University of Calcutta (2005). He also pursued post-doctoral research at KAUST with Prof. Marc G. Genton. His research focuses on advanced statistical methodologies, particularly in Discriminant Analysis, Inference based on Data Depth, Characterization of Multivariate Distributions, and Classification of Sequence Data. His work bridges theoretical statistics with practical applications, developing robust methods for complex data analysis problems across various scientific domains. Dr. Dutta's publication record shows consistent progression from foundational properties of statistical depth functions to practical applications in classification and sequence analysis, demonstrating both theoretical depth and practical relevance in his scholarly contributions. As a faculty member at IIT Kanpur, one of India's premier technical institutions, Dr. Dutta contributes to both teaching and research in the Department of Mathematics and Statistics, helping to advance statistical science education and methodology development.
Subhra Sankar Dhar is an Associate Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur (IIT Kanpur). He has held academic positions at Presidency University and the University of Cambridge, and his expertise lies in non-parametric and robust statistical methods. Education: PhD in Statistics, Indian Statistical Institute, Kolkata (2012) M.Sc. in Statistics, University of Calcutta (2005) B.Sc. (Hons.) in Statistics, Presidency College, Kolkata (2003) Research Interests: His primary research focuses on multivariate quantiles and ranks , data depth , measures of association , and isotonic regression . Secondary interests include likelihood-based inference , Kaplan-Meier estimation , cluster analysis , and missing data analysis . Publications: His recent work spans robust multivariate analysis, non-parametric testing, and robust estimation techniques, reflecting a deep commitment to advancing statistical theory and methodology. Awards & Fellowships: CSIR-UGC Junior Research Fellowship (2005) ISI Kolkata Junior Research Fellowship (2005) Post-doctoral Fellowship, University of Cambridge (2011–2012) Advising & Service: He has supervised numerous master’s projects and is currently supervising PhD students. He has served as a referee for top-tier journals and has been the convenor of STAMATICS, a departmental student group, since 2013. He is also a member of the Scholarship & Prizes Committee and the Seminar & Faculty Search Committee at IIT Kanpur.
S. Akshay is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay . He is affiliated with the IITB Trust Lab , the Ashank Desai Centre for Policy Studies , and the Centre for Formal Design and Verification of Software Systems . His academic background includes a joint PhD from École Normale Supérieure de Cachan and Chennai Mathematical Institute (2010), postdoctoral work at IRISA/ENS Cachan Bretagne (2012) and National University of Singapore (2011), and earlier degrees from ÉNS-Cachan (2006) and Chennai Mathematical Institute (2004). Research interests focus on formal methods , particularly verification of timed, recursive, and distributed systems; automated functional synthesis; formal modeling of probabilistic and dynamical systems; and trust certification in AI models. Applications span systems biology , cyber-physical systems , and artificial intelligence . His recent publications emphasize verification techniques for Markov chains, timed automata, and Boolean functional synthesis, with algorithmic advancements in probabilistic inference, quantifier elimination, and robustness analysis. Professional activities include serving as Treasurer and Council Member of the Indian Association for Research in Computer Science (IARCS). He has co-chaired FSTTCS 2016 and ATVA 2024, and participated in program committees for major conferences like AAAI, CAV, and LICS. Teaching roles at IIT Bombay include courses on Discrete Structures , Formal Models of Concurrent Systems , and Quantitative Verification . Collaborative projects involve institutions such as University of Waterloo, IIT Delhi, and IRISA, Rennes.
Suyash P. Awate serves as the Asha and Keshav Bhide Chair Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Bombay, Mumbai, India. His research spans medical image computing, machine learning, image analysis, computer vision, and statistical modeling and inference. His work focuses on developing novel computational methods for medical image analysis, reconstruction, and quality enhancement. Prof. Awate's research interests center on medical image computing, where he develops advanced machine learning techniques for medical image reconstruction, segmentation, and quality enhancement. His work addresses critical challenges in medical imaging such as low-dose PET/CT imaging, uncertainty quantification in segmentation, robustness to out-of-distribution data, and accelerated MRI/fMRI acquisition. He has pioneered methods combining variational inference, expectation maximization, and deep learning to improve medical image quality while reducing radiation exposure and scan times. His research has significant clinical applications in neuroimaging, tumor analysis, and brain function mapping. His recent publications demonstrate a strong trend toward integrating deep learning with classical statistical methods, particularly focusing on uncertainty-aware models for medical image analysis. His work consistently addresses the challenge of robustness in medical imaging, developing methods that maintain performance even with degraded input data or distribution shifts. A significant portion of his research targets clinical applications, particularly in neuroimaging and oncology, with emphasis on practical implementation for real-world medical settings. Prof. Awate actively mentors PhD, MTech, and BTech students at IIT Bombay. His current PhD students include Vatsala Sharma (awarded Microsoft Research India PhD Award), Jimut Bahan Pal (CMInDS Fellow, Prime Minister's Research Fellow), Tejomay Padole (TCS Research Fellow), Subhankar Nag, and Koustav Pal. He also guides MTech/MS students Soumya Mukherjee, Jay Gorakhiya, and Russel Abreo, along with BTech student Varshith Anumalasetty. He teaches several courses including CS 736: Medical Image Computing (inaugurated in 2014), CS 663: Fundamentals of Digital Image Processing, CS 215: Data Analysis and Interpretation, CS 740: Mathematics for Visual Computing, and CS 101: Computer Programming and Utilization. His laboratory focuses on developing computational methods for medical image analysis, with particular emphasis on uncertainty quantification, robust deep learning for medical imaging, and accelerated acquisition techniques. The research group maintains strong collaborations with medical institutions to ensure clinical relevance of their computational methods.
Amit Mitra is a Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur (IIT Kanpur), India. He has held academic positions at IIT Kanpur since 2005, progressing from Assistant Professor (2005-2007) to Associate Professor (2008-2012) and Professor (2012-present). Prior to this, he served as an Assistant Professor at IIT Bombay (2002-2005) and held research roles at the Reserve Bank of India (1996-2002). His international collaborations include visiting positions at Uppsala University (Sweden) and universities in Australia and Cyprus. His educational qualifications include: Ph.D. in Statistics from IIT Kanpur (1996) M.Sc. in Statistics from IIT Kanpur (1991) B.Sc. (Honors) in Statistics from the University of Calcutta (1988) Professor Mitra's research centers on statistical signal processing with emphasis on parameter estimation for nonlinear time series models, particularly chirp signals, and data mining of financial/economic time series. His work develops robust algorithms for signal model estimation using techniques like genetic algorithms, M-estimators, and wavelet filtering, with applications in radar, sonar, finance, and image processing. He has pioneered methods for 1D/2D chirp signal analysis and volatility modeling in econometrics. His 15 most recent publications (2018-2023) reveal a sustained focus on advanced parameter estimation for chirp and sinusoidal models, with increasing attention to computational efficiency and real-world applicability. Key trends include the development of robust M-periodogram approaches, asymptotic analysis of quantile estimators, and extensions to multidimensional signal processing—particularly for image applications through nonnegative matrix factorization. His scientific awards include: Excellence in Teaching Award (2019, IIT Kanpur) Distinguished Teacher Award (2010, IIT Kanpur) M. N. Murthy Award from the Indian Statistical Institute (2002) Postdoctoral award from Swedish Foundation for Strategic Research (2000) Research award from National Board for Higher Mathematics (1992) Professor Mitra has supervised 67 students including 3 PhD candidates (1 completed), 4 M.Tech students, and 59 Masters students across Mathematics, Economics, and Statistics programs. His applied work includes consultancy projects with Politis (Cyprus) on market data modeling, Retailzoom (Cyprus) on retail analytics, and QuantLink Solutions (USA) on data mining software development for financial applications. No specific research labs or teams are mentioned in the provided text, though his patent in image processing (US Patent 9,940,868) indicates collaborative work with engineers on real-time display systems.
Shalabh is a Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur (IITK), specializing in Statistics and Econometrics. He earned his PhD in Mathematical Statistics from the University of Lucknow and undertook post-doctoral research at Ludwig Maximillians University, Munich, and the University of Pittsburgh. Education: Post-doc in Statistics, Ludwig Maximillians University, Munich, Germany (2007) Post-doc in Statistics, University of Pittsburgh, USA (2002) PhD in Mathematical Statistics, University of Lucknow, India (1996) M.Sc. in Mathematical Statistics, University of Lucknow, India (1992) Undergraduate degree, University of Lucknow, India (1990) Research Interests: His research spans Linear Regression Models , Econometric Modelling , Measurement Error Models , Missing Data Models , Panel Data Models , Restricted Regression , Spatial Models , Forecasting Techniques , and Sampling Theory . He has contributed extensively to the development of improved estimation techniques under measurement errors and missing data scenarios. Publications Trend: Shalabh has published prolifically in top-tier journals, focusing on theoretical and methodological advances in regression analysis, measurement error models, and sampling theory. His work often introduces novel estimation procedures that enhance efficiency and robustness under non-standard conditions. Scientific Awards & Honors: IISA Young Researcher Award 2014 (Theory Category) National Award for Young Statisticians – Professor C.R. Rao Award 2011 Mahalanobis Memorial Medal (MMM) 2008 ISCA Young Scientists' Award 1999 Jan Tinbergan Award 1995 Humboldt Fellowship 2006 BOYSCAST Fellowship 2001 CSIR Junior Research Fellowship 1993 Professional Affiliations: He is a life member of several national and international statistical societies and a Fellow of the Royal Statistical Society, London, since 2000. Contact & Resources: Office: Department of Mathematics & Statistics, IIT Kanpur, Kanpur 208016, India. Email: shalab@iitk.ac.in Phone: +91-512-259-7905
Nivedita Bhaktha serves as an Assistant Professor in the Department of Management Sciences at the Indian Institute of Technology Kanpur (IIT Kanpur). She earned her PhD from The Ohio State University and contributes to the academic community through rigorous research in quantitative methodologies and statistical analysis. Her educational background includes a doctoral degree from The Ohio State University, establishing her expertise in advanced statistical methods and research design. This foundation supports her current academic work at one of India's premier technical institutions. Dr. Bhaktha's research spans multiple specialized areas in quantitative methodology. She focuses on Psychometrics , examining the theory and techniques of psychological measurement. Her work in Data Quality Assessment ensures reliability and validity in data collection processes, while her expertise in Multivariate Analysis reveals complex relationships among multiple variables. She specializes in Latent Variable Modeling for identifying hidden patterns, Categorical Data Analysis for non-numeric variables, and Simulation Studies for testing statistical methods under controlled conditions. Her skills in Data Visualization transform complex analytical results into accessible formats, and her background in Survey Research and Research Methods contributes to robust study design across disciplines. At IIT Kanpur, Dr. Bhaktha plays a vital role in educating future management professionals, bringing advanced statistical expertise to classroom instruction. While specific advisory roles aren't detailed in available information, her research specialization suggests she likely mentors students in statistical methodology and research design.
Purushottam Kar is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He completed his PhD from IIT Kanpur in 2014 and his B.Tech. from the same institution in 2008. His academic career includes a post-doctoral research position at Microsoft Research India from July 2013 to August 2015, following a doctoral research internship there from May to August 2012. Dr. Kar's research focuses on the theoretical and practical aspects of machine learning. His primary interests include Machine Learning , Optimization , and Statistical Learning Theory . More specifically, he works on online and stochastic learning and optimization, multi-label classification, high dimensional statistics, kernel methods, and statistical learning theory. His work bridges theoretical foundations with practical applications in artificial intelligence. His recent publications demonstrate a strong focus on advancing machine learning methodologies, particularly in robust regression, non-decomposable performance measures, precision optimization, multi-label classification, and the theoretical foundations of online learning. His research spans both theoretical developments in statistical learning and practical implementations for complex classification problems. International Conference on Machine Learning (ICML) Reviewer Award (2015) Indian Unit of Pattern Recognition and Artificial Intelligence (IUPRAI) Doctoral Dissertation Award (2014) Yahoo! Key Scientific Challenges (Yahoo! KSC) Program Honourable Mention (2012) Microsoft Research (MSR India) PhD Fellowship (2010) Academic Excellence Awards by IIT Kanpur (2005, 2006) Dr. Kar teaches courses in Machine Learning, Online Learning, and Statistical Learning Theory. His research profile suggests he mentors PhD and Master's students in machine learning and related fields. His work with Microsoft Research indicates significant industry collaborations and grant funding in artificial intelligence research. His office is located in Room 509 of the CSE Extension building (Rajeev Motwani Building) at IIT Kanpur.
Dr. Raghu Nandan Sengupta is a Professor at the Department of Management Sciences, Indian Institute of Technology Kanpur (IITK). With a Ph.D. from IIM Calcutta and a BE in Mechanical Engineering from BIT Mesra, he has held visiting positions at institutions including Princeton University, University of Warsaw, and Technische Universität Dresden. His academic leadership includes serving as Head of Department (2017-2020) and Chairman of GATE/JAM-2025. Research Focus: Sequential Estimation, Statistical Reliability Theory, Risk Analysis, Optimization Techniques in Finance, Meta Heuristic Algorithms, and Robust Optimization His publications span topics like reliability-based portfolio optimization , multiobjective decision-making , and brand sensitivity in B2B marketing . Key awards include DAAD Research Stays, EU-NAMASTE Erasmus Mundus Fellowship, and Indo-US S&T Forum Fellowship. He has advised 16 PhD students and contributed to editorial boards of journals including Sequential Analysis and Foundations of Computing and Decision Sciences . Scientific Awards: DAAD Research Stays (2017, 2015) EU-NAMASTE Erasmus Mundus Fellowship (2015) Indo-US S&T Forum Fellowship (2008) Erasmus Mundus Europe Asia Fellowship (2011) He has taught courses such as Probability and Statistics , Quantitative Methods for Decision Making , and Security Analysis & Portfolio Management at IITK, University of Warsaw, and IGIDR Mumbai. His work bridges statistical inference , financial engineering , and operations research , emphasizing practical applications in industry and academia.
