Mathematical Challenges for Machine Learning and Artificial Intelligence
Join us to discuss current innovations and new opportunities for the mathematical sciences in advancing machine learning and AI.
Developments in the mathematical and statistical sciences are critical to the latest advances in machine learning and artificial intelligence. Join the National Academies for a virtual symposium on Thursday, November 11 from 2-4pm ET to discuss new innovations and future opportunities for mathematical research.
During the symposium, speakers will discuss how statistical theory helps build in protections for black box machine learning models, new approaches for making sense of high-dimensional and heterogenous data, novel methods for understanding causality, and future research opportunities in deep learning.
- Sanjeev Arora, Princeton University
- Emmanuel Candes, Stanford University
- Pragya Sur, Harvard University
- Caroline Uhler, Massachusetts Institute of Technology
- Rebecca Willett, University of Chicago
This symposium is organized by the National Academies’ Board on Mathematical Sciences and Analytics. Learn more about our work and sign up for our mailing list on our website.