Debarghya Ghoshdastidar

Prof. Debarghya Ghoshdastidar is an Assistant Professor of Theoretical Foundations of Artificial Intelligence at Technische Universität München (TUM) since 2019. He holds a Tenure Track position within the TUM School of Computation, Information and Technology. His research focuses on the statistical foundations of machine learning, network science, and their applications in neuroscience, crowdsourcing, and computer vision.

Education: B.E. in Electrical Engineering from Jadavpur University (2010), M.S. and Ph.D. in Computer Science from Indian Institute of Science (2016). Following his PhD, he conducted postdoctoral research at the University of Tübingen, leading a Baden-Württemberg Foundation-funded junior research group.

Research Interests: Theoretical machine learning, network analysis, statistical learning theory, and algorithmic guarantees for AI systems. His work bridges abstract theoretical frameworks with real-world applications, particularly in understanding the robustness and generalization of learning algorithms.

Awards: Recipient of the Baden-Württemberg Elite Program for Postdocs (2017) and Google Ph.D. Fellowship (2013). His contributions include novel algorithms for graph hypothesis testing and spectral hypergraph partitioning.

Grants and Advising: Led a junior research group in Tübingen; no explicit student lists provided here. Active in theoretical contributions to AI, with a focus on interpretable and robust machine learning.