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. See our YouTube playlist. 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.