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MATH-AI: The 3rd Workshop on Mathematical Reasoning and AI
Zhenwen Liang · Albert Q. Jiang · Katie Collins · Pan Lu · Kaiyu Yang · Sean Welleck · James McClelland

Fri Dec 15 07:00 AM -- 03:00 PM (PST) @ Room 217 - 219
Event URL: https://mathai2023.github.io/ »

Mathematical reasoning is a fundamental aspect of human cognition that has been studied by scholars ranging from philosophers to cognitive scientists and neuroscientists. Mathematical reasoning involves analyzing complex information, identifying patterns and relationships, and drawing logical conclusions from evidence. It is central to many applications in science, engineering, finance, and everyday contexts. Recent advancements in large language models (LLMs) have unlocked new opportunities at the intersection of artificial intelligence and mathematical reasoning, ranging from new methods that solve complex problems or prove theorems, to new forms of human-machine collaboration in mathematics and beyond. Our proposed workshop is centered on the intersection of deep learning and mathematical reasoning, with an emphasis on, but not limited to, large language models. Our guiding theme is: "To what extent can machine learning models comprehend mathematics, and what applications could arise from this capability?'' To address this question, we aim to bring together a diverse group of scholars from different backgrounds, institutions, and disciplines in our workshop. By hosting this workshop, we hope to stimulate insightful discussions that will guide future research and applications in this rapidly expanding field.

Author Information

Zhenwen Liang (University of Notre Dame)
Albert Q. Jiang (University of Cambridge; Mistral AI)
Katie Collins (University of Cambridge)
Pan Lu (UCLA)
Kaiyu Yang (California Institute of Technology)
Kaiyu Yang

Kaiyu Yang is a postdoctoral researcher at Caltech in the Computing + Mathematical Sciences (CMS) Department, working with Prof. Anima Anandkumar. His research aims to make machine learning capable of symbolic reasoning. It includes (1) applying machine learning to symbolic reasoning tasks, such as automated theorem proving; and (2) introducing symbolic components into machine learning models to make them more interpretable, verifiable, and data-efficient. In addition, he has also worked on constructing and analyzing machine learning datasets, especially focusing on fairness, privacy, and mitigating dataset bias. His research is recognized with a Siebel Scholar award. Before joining Caltech, he received his Ph.D. from the Department of Computer Science at Princeton University, advised by Prof. Jia Deng.

Sean Welleck (University of Washington)
James McClelland (Stanford University)

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