Hayden Schaeffer is a Professor of Mathematics at the University of California, Los Angeles. His research is in mathematical machine learning, differential equations, randomization, and physical modeling.
Mathematical Sciences Building 7931
hayden at math dot ucla another dot edu
Multi-Operator Learning and Equation Generation
Operator Learning and Multi-Scale Physics
High-contrast and multi-scale phenomena are challenging to capture with standard ML and deep networks. We work on operator learning approaches to handle challenges for stationary solutions and dynamic multi-scale problems.
See our recent paper: https://arxiv.org/abs/2308.14188
Machine Learning Theory
We are developing theoretical understanding of AI by studying the accuracy, stability, and reliability of neural networks and randomized methods. This includes providing theoretical guarantees, conditioning, and generalization of learning algorithms.
See our recent work on randomized feature analysis: