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Abstract
Artificial intelligence (AI) and machine learning (ML) offer significant potential to revolutionize the fundamentals of scientific computation and discovery. In recent years, AI-driven approaches have begun to reshape how we understand, prove, and optimize mathematical systems — from symbolic reasoning and theorem proving to mathematical optimization and numerical analysis.
Objective
The goal of this seminar course is to expose students to the rapidly developing field of AI for Science, with a particular focus on applications in mathematics and optimization. Through readings, discussions, and hands-on projects, students will explore how modern AI tools (e.g., large language models, neural networks, reinforcement learning, and symbolic AI) can assist in mathematical discovery, automate problem solving, and generate new insights into open problems. By the end of the course, each student will complete a research-oriented project that applies AI methods to a mathematical or optimization challenge — for instance verifying proofs, discovering patterns, or accelerating solvers. The seminar aims to develop students’ skills in presenting scientific results, both in written and oral form, and to inspire creative uses of AI in advancing the frontiers of mathematical research.