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 research scientist at Meta working on Language Modeling, Causal Inference, and Applied Reinforcement learning. Her recent work in world models for coding at FAIR builds upon a robust body of prior work in causal inference and biases, 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.

Elizabeth Polgreen is an assistant professor at the University of Edinburgh. She is interested in formal program synthesis techniques and the use of learning and synthesis to increase the scalability of verification.

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.