Talks Upcoming and Last 12 Months

1. September - December 2024 - Los Angeles for IPAM program on Mathematics of Intelligence

2. October 9, 2024 - Yale for a seminar

3. October 21 - October 25, 2024- Atlanta for SIAM conference on Mathematics of Data Science

4. November 4, 2024 - Tufts for a seminar

5. November 14 -  November 16, 2024 - Philadelphia for DeepMath

6. December 11 - December 15, 2024 - Vancouver for NeurIPS

7. December 16, 2024 - London for the session on “Over-parameterization in ML” at the CFE-CMStatistics conference

8. January 2025 - Seattle for JMM, organizing a session on Geometry and Combinatorics for Machine Learning with Eli Grigsby and Kathryn Lindsey. 

9. January 2025 - Ethereum Foundations Eval Science Primer Series

10. March 2025 -  Boston College, Computer Science Seminar

11. April 2025 - UCLA, Math Colloquium

12. July 2025 - ICML Workshop on High Dimensional Learning Dynamics

13. August 2025 - SIAM Annual Meeting, sessions on Double Descent

14. September 2025 - University of Massachussets Lowel Seminar

15. September 2025 - Hamburg for Conference on Mathematics of Machine Learning

16. Novermber 2025 - Univerisity of Pittsburgh, Workshop on Mathematical Analysis and Machine Learning. 

17. March 2026 - Organizing MFO workshop on Novel and Modern Phenomena in Machine Learning

Research Interests

I am broadly interested in math for machine learning. I think math for machine learning can be used in one of two ways.

1. We use math to analyze existing methods and prove theoretical results. These results help us gain insights into techniques that are currently being used. 

   • In this area, I am primarily interested in how regularization affects generalization. This would include both explicit regularization, such as ridge regularization,  early stopping, and regularization via noise, as well as implicit regularization from the parameterization, optimization and the loss landscape.  

   • I am also interested in other questions relating to universal approximation. 

2. We can use math to design new principled methods.

   • I am interested in designing new geometric deep learning techniques using ideas from differential geometry and coarse geometric group theory. 

   • I am also interested in generative AI, and using ideas from geometry and random matrix theory to improve their design.