Keynote Speakers

Current deep learning systems excel at perceptual recognition, but struggle with causal reasoning and verifiable safety guarantees. In this keynote, we explore how inductive biases, conscious priors, and generative flow networks (GFlowNets) guide us toward reliable, agentic reasoning models aligned with human welfare.

Prof. Yoshua Bengio is recognized worldwide as a pioneer in deep learning and artificial intelligence. He is the co-recipient of the 2018 ACM A.M. Turing Award and the founder and scientific director of Mila.

As Dennard scaling slows, continued efficiency gains demand aggressive heterogeneity across cloud-to-edge tiers. This talk presents recent breakthroughs in domain-specific accelerators, memory hierarchies, and compiler-driven scheduling techniques designed to drastically curb energy consumption in computing workloads.

Prof. Margaret Martonosi is a member of the US National Academy of Engineering. Her research focuses on computer architecture and hardware-software interface techniques for energy-efficient computing systems.

Data science algorithms increasingly drive consequential societal decisions. Raw accuracy metrics often conceal structural biases, privacy intrusions, and systemic hallucinations. We examine the full data lifecycle and discuss architectural frameworks ensuring transparent, equitable outcomes.

Prof. H.V. Jagadish is Director of the Michigan Institute for Data Science (MIDAS) and an ACM and IEEE Fellow with over 200 major publications in database systems and data ethics.