Venue: Bharti-501/ MS Teams Abstract: Building artificial intelligence with meaningful real-world impact requires models that can understand and interact in both the virtual and physical world. This talk outlines four key capabilities necessary to achieve this: foundational world knowledge, alignment with human values and expectations, reasoning ability, and the capacity to self-improve. The talk begins with… Read More »Advancing Safe Multimodal Intelligence by Dr. Pritam Sarkar

Venue: Bharti501 Abstract: Over the last decade, confidential computing has emerged as an advanced security and privacy primitive that can fundamentally change the nature of trust that we place in digital services. In this talk, we will take a journey through the evolution of confidential computing and understand its current state and open problems that… Read More »State of Confidential Computing by Dr. Kapil Vaswani

Venue: Bharti501 Abstract: Memory bandwidth scaling has emerged as a critical bottleneck as compute capabilities continue to grow faster than DRAM performance. This talk examines why higher memory frequencies do not directly translate to higher delivered bandwidth, focusing on bandwidth efficiency rather than peak bandwidth. It discusses key constraints arising from DRAM organization, timing parameters,… Read More »Challenges in scaling memory bandwidth in modern SoCs by Nithya Bashyam

Venue: Bharti501 Abstract: Secure Multi-Party Computation (MPC) is a central problem in cryptography, often regarded as its holy grail. It enables a group of mutually distrusting data owners to jointly compute a function over their private inputs, while revealing nothing beyond what is inherently implied by the output itself. Round complexity is one of the… Read More »The Landscape of Exact Round Complexity in Secure Multi-Party Computation by Prof. Arpita Patra

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