Distinguished Seminar in Optimization & Data (DSOD)

I am a co-organizer of the Distinguished Seminar in Optimization & Data (DSOD), an interdepartmental talks series at the University of Washington, focused on all aspects of optimization and data science. The speakers of 2023 (see website for details):

Date	Speaker	Title
1/9/23	Éva Tardos, Cornell University	Stability and Learning in Strategic Queueing Systems
4/3/23	Damek Davis, Cornell University	Leveraging "partial" smoothness for faster convergence in nonsmooth optimization
5/1/23	Misha Belkin, University of California, San Diego	The Challenges of Training Infinitely Large Neural Networks
5/22/23	Philippe Rigollet, MIT	Statistical applications of Wasserstein gradient flows
6/5/23	Ting-Kei Pong, Hong Kong Polytechnic University	The Challenges of Training Infinitely Large Neural Networks
10/2/23	Ryan O'Donnell, Carnegie Mellon University	New directions in (quantum) distribution learning and testing
11/6/23	Lin Xiao, Fundamental AI Research - Meta	Non-negative Gauss-Newton Methods for Empirical Risk Minimization

Machine learning and optimization seminar (ML-OPT)

The machine learning and optimization seminar is a venue for internal and external speakers to present their work on machine learning and data science. It takes place every Friday at 1:30 pm and is primarily intended for graduate students and post-docs to publicize their work. Join the mailing list here. This seminar is supported by the Institute for the Foundations of Data Science (IFDS), an NSF program I am a co-PI of. Please drop me an email if you think you may want to speak at this seminar.