Swarat Chaudhuri is a Professor of Computer Science at UT Austin and a Senior Staff Research Scientist at Google DeepMind. His research lies at the interface of programming languages, formal methods, and machine learning. His research seeks to develop a new class of intelligent systems that are reliable, transparent, and secure by construction and can solve reasoning-intensive tasks beyond the scope of contemporary AI.

Weizhu Chen is a Technical Fellow and CVP at Microsoft. He leads a team working on large-scale (OpenAI and Microsoft) model training, including post-training, pre-training, and data. His published works include LoRA, DeBERTa, Phi, and Rho-1 (best paper runner-up award in NeurIPS 2024).

Yejin Choi is the (incoming) Dieter Schwarz Foundation Professor and HAI Senior Fellow at Stanford. She is a Senior Director at NVIDIA. Choi is MacArthur Fellow, AI2050 Senior Fellow, and named among Time 100 Most Influential People in AI in 2023. Her current research interests include fundamental limits and capabilities of LLMs, alternative training recipes for language models, symbolic methods for neural networks, reasoning and knowledge discovery, moral norms and values, pluralistic alignment, and AI safety.

Leo is a Senior Principal Applied Scientist in the Automated Reasoning Group at AWS. He is also the Chief Architect of the Lean FRO, a non-profit organization dedicated to driving Lean towards long-term sustainability. Before joining AWS, he worked at Microsoft Research and SRI International. His research areas are automated reasoning, theorem proving, decision procedures, SAT, and SMT. He is the main architect of several automated reasoning tools: Lean, Z3, Yices 1.0, and SAL. Leo's work in automated reasoning has been acknowledged with a series of prestigious awards, including the CAV, Haifa, and Herbrand awards, as well as the Programming Languages Software Award by the ACM. Leo's work has also been reported in the New York Times and many popular science magazines such as Wired, Quanta, and Nature News.

Hanna Hajishirzi is the Torode Family Associate Professor in the Allen School of Computer Science and Engineering at the University of Washington and a Senior Director of NLP at AI2. Her research delves into various areas within NLP and AI, focusing on understanding and pushing the boundaries of large language models. She is a recipient of 2020 Alfred Sloan Fellowship, 2021 NSF CAREER award, 2019 Intel rising star award, 2018 Allen Distinguished Investigator award, 2023 Academic Achievement UIUC Alumni award, 2024 innovator of the year award finalist by GeekWire, and several research faculty awards from industry.

Chi Jin is an Assistant Professor of Electrical and Computer Engineering at Princeton University. His research focuses on the decision-making aspects of machine learning, developing intelligent agents capable of complex strategy, advanced reasoning, and planning. His research interests also include improving the reasoning abilities of LLMs and developing LLM agents for mathematics, coding, and complex games.

Aviral Kumar is an Assistant Professor in the School of Computer Science at Carnegie Mellon University. He is interested in a broad range of topics ranging from core reinforcement learning (RL) algorithms to scaling RL methods to foundation models to real robots.

Weiyang Liu is an (incoming) Assistant Professor at CUHK. He works on principled modeling of inductive bias in learning algorithms. His research seeks to understand how inductive bias affects generalization, and to develop ``light-yet-sweet'' learning algorithms: (i) light: conceptually simple in methodology and easy to implement in practice, (ii) sweet: having clear intuitions and nontrivial theoretical guarantees.

Tengyu Ma is an Assistant Professor of Computer Science and Statistics at Stanford. His research interests broadly include topics in machine learning, algorithms and their theory, such as deep learning, (deep) reinforcement learning, pre-training / foundation models, robustness, non-convex optimization, distributed optimization, and high-dimensional statistics.

Martin Vechev is a Professor of Computer Science at ETH Zürich where he leads the Secure, Reliable, and Intelligent Systems Lab. He is also the Founder and Architect of INSAIT, the first world-class research center in computer science and artificial intelligence in Eastern Europe. His work spans the broad intersection of artificial intelligence and programming languages, including both theoretical and system aspects.