Team

Avisek Gupta is a machine learning researcher focused on unsupervised and weakly supervised learning, particularly how low-effort expert supervision can guide statistical models. His recent projects span Alzheimer’s disease classification, audio speaker identification, and multi-agent coalition formation. He earned his Ph.D. in Computer Science from the Indian Statistical Institute (ISI), Kolkata, in 2021. Dr. Gupta recently served as a Postdoctoral Fellow at IAI (2022–2025), where he taught Deep Learning, Machine Learning, and Statistics, and previously worked as a Visiting Scientist at ISI Kolkata. He is also an instructor and advisory committee member for ISI Kolkata’s annual Winter School on Deep Learning

Subhajit Saha is a Ph.D. researcher specializing in Reinforcement Learning. His research focuses on Multi-Agent Reinforcement Learning, Deep Reinforcement Learning, and their applications to real-world challenges. His work aims to advance intelligent decision-making systems through principled learning, optimization, and data-driven methodologies.

Rupak Chain is a PhD student specializing in theoretical computer science and mathematics at the University of Warsaw’s Faculty of Mathematics, Informatics and Mechanics (MIMUW). His research bridges pure mathematics and computational applications. Working under the supervision of Dr. Kunal Dutta and supported by an NCN OPUS grant, his primary focus of research is on dimensionality reduction and sample compression, including VC theory and high-dimensional probability. To tackle these problems, he utilizes tools from topological data analysis (TDA) and algebraic topology. His theoretical interests also extend to randomized algorithms, terminal embeddings, and the Johnson-Lindenstrauss lemma. He holds a Master of Mathematics (M.Math) from the Indian Statistical Institute (ISI) Kolkata and a B.Sc. (Honours) in Mathematics from Ramakrishna Mission Vidyamandira.

Tanmoy Jana is currently a PhD student in Artificial Intelligence and Machine Learning at TCG Crest. His research interests lie in machine learning, particularly in subspace learning, sparse representation, kernel methods, and robust statistical learning. He is interested in developing optimization-based frameworks that enhance the robustness, stability, and generalization capabilities of machine learning models in challenging real-world settings.