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Caltech

Mathematics & Machine Learning Seminar

Tuesday, February 27, 2024
2:00pm to 3:00pm
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East Bridge 114
Leveraging ML to identify structures in knot theoretic data
Mark Hughes, Department of Mathematics, Brigham Young University,

Knot theorists have a long history of compiling tables of knots and their invariants. These invariants come from a variety of sources (including physics and biology) and involve constructions which are algebraic, analytic, geometric, and combinatorial in nature. Despite the subject's long history, there are many fundamental open questions about these invariants that have yet to be answered by classical techniques.

In recent years, machine learning has emerged as a viable approach to studying many of these problems. This approach raises new questions about the structures underlying these knot data sets. In this talk I will outline questions and results at the intersection of knot theory and ML, and describe approaches to using reinforcement learning to solve difficult generative problems in knot theory.

For more information, please contact Math Department by phone at 626-395-4335 or by email at [email protected].