Geometric Machine Learning and Applications in the Sciences
Presenter: Melanie Weber (Harvard University)
Title: Geometric Machine Learning and Applications in the Sciences
Date/Time: Tuesday, June 18th, 9:30 - 11:00 AM
Abstract: Many machine learning and data science applications involve data with geometric structure, such as graphs, strings, and matrices, or data with symmetries that arise from fundamental laws of physics in the underlying system. In this talk we discuss how we can identify such structure in data and models using geometric tools, as well as examples of leveraging such structure for the design of more efficient machine learning algorithms. We will also discuss applications of such methods in the Sciences.
Biography: Melanie is an Assistant Professor of Applied Mathematics and of Computer Science at Harvard University. Her research focuses on utilizing geometric structure in data for the design of efficient Machine Learning and Optimization methods. In 2021-2022, she was a Hooke Research Fellow at the Mathematical Institute in Oxford. Previously, she received her PhD from Princeton University (2021). She is the recipient of the IMA Leslie Fox Prize in Numerical Analysis (2023) and a Sloan Fellowship in Mathematics (2024).