Peiyang Song is a PhD student at Carnegie Mellon University. His research focuses on mathematical reasoning, theorem proving, and natural language reasoning, with an emphasis on integrating formal systems and large language models. He has received various awards, including an IEEE Micro Top Pick Award, a Best Paper Award at the ICML CTB Workshop, and a Best Paper Honorable Mention at the NeurIPS LAW Workshop. He was also a lead instructor for the Tutorial on Neuro-Symbolic Theorem Proving with Lean at the 3rd Neuro-Symbolic AI Summer School (NSSS).

Kaiyu Yang is a Chief Scientist at Apodex, working on enhancing AI's capabilities in mathematical reasoning by integrating formal systems such as Lean. His research explores how machine learning and large language models can generate mathematical conjectures, prove theorems, write verifiable code, and perform reasoning that combines natural and formal languages. He organized the MATH-AI Workshop at NeurIPS'23 & '25, and the Tutorial on Machine Learning for Theorem Proving at NeurIPS'23.

Pan Lu is a Postdoctoral Scholar at Stanford University. He received his Ph.D. in Computer Science from UCLA. His research focuses on machine reasoning, particularly in mathematical reasoning and scientific discovery. He has received various awards, including ACL 2023 Best Paper Honorable Mention, KnowledgeNLP Workshop 2025 Best Paper Award, Most Influential NeurIPS Papers, Amazon PhD Fellowship, Bloomberg PhD Fellowship, and Qualcomm Innovation Fellowship. He served as senior program chair for NENLP'25 and program chair for SoCal NLP'23. He has extensive experience in organizing workshops, including the MATH-AI Workshop at NeurIPS from 2022 to 2025 and the AI for Math Workshop at ICML'24.

Patrick Shafto is a Professor of Mathematics and Computer Science at Rutgers University, a Program Manager at DARPA, and the Founder and Scientist-at-Large at Redpoll. His research focuses on the computational principles of human learning, reasoning, communication, and scientific discovery, spanning cognitive science, artificial intelligence, and machine learning. He is a Fellow of the Cognitive Science Society and co-organized the IPAM long program on Mathematics of Intelligences in Fall 2024. He has also organized or co-organized 9 conferences, symposia, and workshops on explanation, guided learning, pedagogical reasoning, and inductive reasoning.

Sanjeev Arora is a Professor of Computer Science at Princeton University, where he did foundational work in theoretical computer science, complexity theory, and machine learning theory. He has received major honors such as the Gödel Prize, ACM Prize in Computing, and election to the National Academy of Sciences. He is also a founding director of Princeton Language and Intelligence (PLI).

Katie Collins is a postdoctoral fellow in the Computational Cognitive Science Group at MIT, a visiting postdoc at the Princeton AI Lab, and a Research Affiliate at the Centre for Human-Inspired AI at the University of Cambridge. Her research focuses on cognitive science, artificial intelligence, human-AI interaction, and mathematical reasoning, including evaluating language models for mathematics through interactions with mathematicians. She organized the MATH-AI Workshop at NeurIPS'23.

Sean Welleck is an Assistant Professor in the Language Technologies Institute at Carnegie Mellon University. His research focuses on language modeling, reasoning, controllable generation, and machine learning for formal reasoning, including applications to mathematical reasoning and theorem proving. He has extensive workshop organization experience, including AI for Math at ICML'24, MATH-AI at NeurIPS'22 & '23, MathNLP at EMNLP'22, Math AI for Education at NeurIPS'21, VerifAI at ICLR'25, and SCALR at COLM'25.

Mateja Jamnik is a Professor of Artificial Intelligence in the Department of Computer Science and Technology at the University of Cambridge and an associate fellow of the Leverhulme Centre for the Future of Intelligence. Her research focuses on human-like AI, automated reasoning, diagrammatic reasoning, knowledge representation, theorem proving, explainable AI, and cognitive science. She founded the women@CL initiative, serves on the UK Home Office Science Advisory Council, and previously served as Specialist Adviser to the House of Lords Select Committee on Artificial Intelligence.