Venue: Bharti501 Abstract: This talk will provide a perspective on where we are at IBM Quantum in building useful quantum computers. There are two main pillars: developing a quantum computing platform that scales beyond classical computers, and discovering algorithms that leverage the strengths of this platform to deliver state-of-the-art methods for solving hard problems. The… Read More »Quantum Computing: Towards Advantage by Dhinakaran Vinayagamurthy

Venue: SIT001 Online joining: https://teams.microsoft.com/meet/48853006918605?p=788lqF84K1ykLq1rtg Abstract: Standard supervised learning on neuroimaging data optimizes for diagnostic prediction while yielding feature-level importance scores that lack network-level, assessment-specific interpretability required for biomarker discovery; while unsupervised methods reduce data dimensions leading to loss of assessment-specific information. This talk presents frameworks developed towards addressing these gaps via biologically interpretable methodologies for… Read More »Learning assessment-aware brain representations from multimodal neuroimaging data by Dr. Ishaan Batta

Online joining: https://teams.microsoft.com/meet/44299089959938?p=5wwN132pf54i4sVnyN Abstract: Successful engagement with the physical world involves perceiving the underlying structure, predicting how things unfold, and planning actions accordingly. This rich understanding and reasoning about our physical environment, or 'intuitive physics', develops early in infancy and is a core component of human cognition. While it seems easy for us to understand and… Read More »Physical reasoning in Minds, Brains, and Machines by Dr. Pramod RT

Venue: SIT001 Abstract: Solving long-horizon tasks remains a central challenge in robotics because agents must explore efficiently, assign credit over long time scales, and act under sparse supervision. To address this, hierarchical reinforcement learning (HRL) offers a promising alternative to flat reinforcement learning (RL) by enabling a high-level policy to propose subgoals and a low-level… Read More »Learning Hierarchical Control via Feasible Subgoal Prediction by Utsav Singh

Venue: Bharti-501/ MS Teams Abstract: In-Context Learning (ICL) is a remarkable phenomenon whereby transformer-based LLMs can use data contained within their prompts to adapt their responses, without changing their weights. This suggests that such models encode learning mechanisms. The recent literature has used statistical learning problems as a test-bed to investigate ICL, and established that ICL can… Read More »Algorithmic Behaviours in In-Context Learning by Dr. Aditya Gangrade