# Seminar on Math, Stat, and AI: Matus Telgarsky #### calendar\_today Date and Time **April 3, 2026** 10:30AM EDT #### pin\_drop Location **Maxwell-Dworkin G125** [33 Oxford St Cambridge , MA 02155 United States ]() The Seminar on Math, Stat, and AI is an interdisciplinary seminar series focusing on problems at the intersection of statistics, probability, artificial intelligence and related fields.The upcoming seminar takes place on Friday, April 3rd at 10:30am in **Maxwell-Dworkin G125**. This week's speaker will be **Matus Telgarsky,** assistant professor at the Courant Institute of Mathematical Sciences of NYU. **Implicit bias results for Muon, Adam, and Friends** This talk will give both an empirical overview and a few simple bonds controlling the optimization path, or implicit bias, of modern optimization methods such as Adam and Muon (and Friends). The talk will begin with empirical results demonstrating the implicit bias phenomenon with shallow networks and also transformers combined with chain-of-thought. The talk will then briefly survey a few mathematical implicit bias analyses of nonlinear networks, which unfortunately do not carry through to transformers. As such, the talk concludes with a technical portion presenting another approach to analyzing these optimization methods in the linear case, providing generic implicit bias results for them, and empirically demonstrating hope that this particular methodology can carry over to the nonlinear case. Bio: Matus Telgarsky is an Associate Professor of Computer Science at the Courant Institute at NYU, specializing in deep learning theory. Matus completed his PhD under Sanjoy Dasgupta at UCSD; subsequent adventures include co-founding the Midwest ML Symposium in 2017 with Po-Ling Loh, and co-chairing two Simons Institute Programs at UC Berkeley. Accolades include receiving a 2018 NSF Career Award and delivering a COLT 2025 keynote. Share on:- [ Facebook ](#) - [ Twitter ](#) - [ Linkedin ](#) Save: [ Add to calendar calendar\_today ](https://statistics.fas.harvard.edu/node/1636591/event-feed.ics) Copy link link