March 9, 2026
Emre Kiciman (Microsoft)
"Advances in Causal Reasoning with LLMs"
Correct causal reasoning requires domain knowledge beyond observed data. Framing cause-and-effect questions in medicine, science, law, and engineering depends on eliciting expert assumptions about system...
March 10, 2026
Elad Hazan (Princeton University)
"Provably Efficient Learning in Nonlinear Dynamical Systems via Spectral Transformers"
Learning in dynamical systems is a fundamental challenge underlying modern sequence modeling. Despite extensive study, efficient algorithms with formal guarantees...
March 16, 2026
Manil Maskey (NASA)
"Artificial Intelligence for Science at NASA: Accelerating Discovery"
Artificial Intelligence (AI) is significantly transforming the process of scientific discovery. This talk will describe how AI grounded in science rigor is enabling NASA science...
April 1, 2026
Urbashi Mitra (USC)
"Causal Graph Inference: New methods for Application-driven Graph Identification, Interventions and Reward Optimization"
Causal inference enables understanding of the underlying mechanisms in complex systems, with applications spanning social...
April 6, 2026
Eric Mazumdar (Caltech)
“Behavioral Economics as a Foundation for Principled Multi-Agent Reinforcement Learning: Tractability, Robustness, and Other Free Lunches”
Emerging applications in AI are fundamentally multi-agent, yet little guidance exists for designing agents for...
April 7, 2026
Allison Koenecke (Cornell University)
"Algorithmic Decisions in the SNAP Benefits Pipeline"
America’s Supplemental Nutrition Assistance Program (SNAP), formerly known as food stamps, helps low-income households buy nutritious food. Social workers provide pivotal support at many points...
April 13, 2026
Suhas Diggavi (UCLA)
"A Statistical Framework and Algorithms for Personalized Federated Learning"
In federated learning, edge nodes collaboratively build learning models from locally generated data. A unique challenge in Federated learning (FL) is heterogeneous data...
April 21, 2026
Jeff Shamma (University of Illinois Urbana-Champaign)
A control-theoretic perspective on game-theoretic learning
The framework of multi-agent game-theoretic learning explores how individual agent strategies evolve in response to the strategies of others. A central question is whether these evolving...
