Speaker: Michael Puthawala — South Dakota State
Title: Jaywalking at the Intersection of Machine Learning and Interesting Math
Abstract: Machine learning (ML) is a powerful tool for solving problems in fields ranging from robotics, medicine, materials science, cosmology and beyond. I’ll try and convince you that it’s mathematically interesting too. No prior knowledge of machine learning or deep learning is needed, but a general distrust of flashy claims on artificial intelligence may help. I will first give an overview of ML and some of its applications. Next, I will present two vignettes showing how ‘practical’ problems in ML yield mathematical problems with satisfying and interesting answers. First, we’ll consider how ‘one simple computational trick’ discovered to improve the speed of convergence of training makes a statement about knot theory. Second, I will show how a desire to build ML models to shortcut PDE simulations yield the development of neural operators that ‘properly’ approximating integral operators on a Hilbert space.