Invited Speakers & Panelists
Jia Li
Project Numina
Jia Li is the Founder of Project Numina, an open-source organization dedicated to advancing AI for mathematics. He previously served as a researcher at Mistral AI and was the Founder and CTO of Cardiologs, an AI-driven medical diagnostics company founded in 2014 and acquired by Philips in 2022. At Project Numina, he leads the development of large-scale language models for mathematical reasoning and formal theorem proving, including Kimina-Prover, the first large language model designed specifically for formal reasoning in proof assistant environments. Numina's models won the first AIMO Progress Prize awarded by the AIMO Committee. His work focuses on building open, verifiable AI systems for formal mathematics and scientific discovery.
Dawn Song
UC Berkeley
Dawn Song is a Professor in Computer Science at UC Berkeley and Co-Director of Berkeley Center for Responsible Decentralized Intelligence. Her research interests lie in AI safety and security, Agentic AI, deep learning, and security and privacy. She is the recipient of the MacArthur Fellowship and the Guggenheim Fellowship, and has been recognized as Most Influential Scholar in computer security. She is an ACM Fellow and IEEE Fellow.
Leonardo de Moura
AWS / Lean FRO
Leonardo is a Senior Principal Applied Scientist in the Automated Reasoning Group at AWS and the Chief Architect of the Lean FRO, which he co-founded with Sebastian Ullrich. Before AWS, he spent 17 years as a Senior Principal Researcher in the RiSE group at Microsoft Research, and earlier was a Computer Scientist at SRI International. His work spans automated reasoning, theorem proving, decision procedures, and SAT and SMT, and he is the main architect of Lean, Z3, Yices 1.0, and SAL. His contributions have been recognized with the CAV, Haifa, and Herbrand awards and the ACM Programming Languages Software Award.
Emily First
Rutgers University
Emily First is an Assistant Professor in Computer Science at Rutgers University. Her research focuses on AI for software engineering and programming languages, particularly leveraging AI for theorem proving. She works on creating tools to automatically generate proofs and lemmas in proof assistant languages such as Rocq, Isabelle/HOL, and Lean, and designs effective interfaces for human-AI interaction. She completed her PhD at UMass Amherst and was a postdoctoral researcher at UC San Diego.
Zheng Yuan
ByteDance Seed
Zheng Yuan is a researcher in ByteDance Seed. He leads the development of Seed Prover, which proves 5/6 problems on IMO 2025. His research interest is training long-horizon agents for formal and informal mathematics. He earned his Ph.D. degree from Tsinghua University.
Yinya Huang
ETH AI Center, ETH Zurich
Yinya Huang is a Postdoc Fellow at the ETH AI Center, ETH Zurich. She received her Ph.D. in Computer Science from Sun Yat-sen University. Her research focuses on AI for mathematics and science, with emphasis on mathematical reasoning, formal theorem proving with LLMs, and reliable machine learning. She has published in top venues including TPAMI, NeurIPS, ICLR, ACL, and EMNLP, and is a recipient of the 2025 ETH Career Seed Award and the 2024 ACM SIGCSE China Excellent Doctoral Dissertation Award.
Sorrachai Yingchareonthawornchai
ETH Zurich
Sorrachai Yingchareonthawornchai is a Junior Fellow at the Institute for Theoretical Studies, ETH Zurich and Tech Lead of CSLib. His research interests lie in the design of fast graph algorithms, combinatorial optimization, and combinatorics of computation. He earned his Ph.D. in Computer Science from Aalto University in 2023, and his doctoral work was recognized with the Doctoral Thesis Award 2023 from Aalto University's School of Science. Previously, he was a postdoctoral researcher at the Hebrew University of Jerusalem and a Simons-Berkeley postdoctoral fellow at UC Berkeley.
Siyuan Guo
Prior Labs
Siyuan Guo is an AI scientist at Prior Labs. She earned her PhD jointly from the University of Cambridge and the Max Planck Institute for Intelligent Systems. Her research focuses on the physics of learning, causal inference, and AI for scientific discovery. She has published at top-tier venues with 1 oral and 2 spotlight presentations. Her research has been recognized with honors including MIT EECS Rising Star and the G-Research PhD Prize.
