Antoine Bosselut (EPFL) is advancing how AI systems can reason about the world through language. His work on commonsense reasoning and knowledge-augmented models aims to bridge large language models with human-like understanding.

Arnaud Doucet (Oxford) is a pioneer in Bayesian statistics and machine learning. His research has laid the foundation for many modern AI algorithms, particularly in probabilistic modeling and sequential inference.

Yarin Gal (Oxford) leads the Oxford Applied and Theoretical Machine Learning Group. He is known for his influential work in Bayesian deep learning, focusing on how to model and manage uncertainty in AI systems—an essential step toward safe deployment in real-world settings.

Dawn Song (UC Berkeley) is a globally recognized authority in AI security and privacy. A MacArthur Fellow and serial entrepreneur, her research spans deep learning, trustworthy AI, and secure computing platforms, bridging research with impactful real-world applications.

Mengdi Wang (Princeton) explores reinforcement learning and stochastic optimization. Her research addresses how AI systems can learn to make complex decisions in uncertain environments, with applications in healthcare, logistics, and operations.

Diyi Yang (Stanford) focuses on human-centered natural language processing. She leads research into socially aware and equitable AI systems, working on fairness, bias, and social dynamics in language technologies.

These speakers represent the forefront of AI research and innovation, each contributing unique insights into the challenges and possibilities of artificial intelligence.

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