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Title: An Information Theoretic Look into AI: Conformal Prediction and Reasoning 

Time: 10:30am on 12th May, Tuesday, 2026 

Location: 611 EEE building, South Kensington Campus

Abstract: When agents and systems operate under additional constraints, a fundamental question concerns the limits of achievable performance and the emergent behaviors induced by those constraints. Such questions are central to information theory: for example, lossy compression under distortion constraints or communication under power or amplitude constraints. Characterizing these fundamental limits is essential both for evaluating proposed algorithms and for deriving principled design guidelines. In this work, we examine several analogous problems in the AI domain, including conformal prediction under efficiency (prediction set size) constraints, the confidence-efficiency trade-off in transductive learning, and efficient neural reasoning. We study these problems through an information-theoretic lens, establishing fundamental bounds and providing design principles.

Short Bio: Arash Behboodi is Director of Engineering at Qualcomm AI Research, where he has led several major initiatives, including the Qualcomm 5G AI Suite and Efficient Reasoning on the Edge. His research contributions span information theory, compressed sensing, and machine learning for wireless communication, learning theory, and machine learning for inverse problems. He did pioneering works on differentiable simulation for wireless propagation modeling. He has received multiple best paper awards, including recognition at venues such as Asilomar and Qualcomm IP Achievement Award. Arash earned his PhD in Information Theory from École Supérieure d’Électricité (Supélec, now CentraleSupélec), and his bachelor's and master's degrees from Sharif University of Technology. He also holds a Master’s degree in Philosophy from Panthéon-Sorbonne University, where his work focused on the philosophy of language.