Gabriel Poesia is an incoming Assistant Professor at the University of Michigan, a current Research Fellow at Harvard University's Kempner Institute, and a recent graduate of Stanford University. He works on building self-improving AI systems that are capable of formal reasoning and open-ended discovery.

Emily McMilin is currently a researcher at Meta FAIR working on post-training data and methods for code generation and coding agents. Her recent work in world models for code at FAIR builds upon a robust body of prior work in causal inference and LLMs, many contributions of which came as a single-author independent researcher.

Federico Mora Rocha is an Applied Scientist in the Automated Reasoning Group at Amazon Web Services, an incoming Assistant Professor at the University of Waterloo and a Faculty Affiliate at the Vector Institute. His research is centerd around automated reasoning, programming languages and their interactions with neuro-symbolic systems.

Aviral Kumar is an Assistant Professor in Computer Science and Machine Learning at Carnegie Mellon University, where he directs the CMU AI & Reinforcement Learning lab. He completed his Ph.D. at UC Berkeley in 2023. His research spans core reinforcement learning algorithms, scaling RL methods to foundation models, and applications to real robots.

Naman Jain is a PhD student at UC Berkeley and researcher at Cursor AI. His research, focused on evaluation of and reinforcement learning environments for LLM coding agents, includes prominent benchmarks such as LiveCodeBench and an open-sourced framework that turns any GitHub repository into test environments for coding agents.