Research Talks

  • Tackling Distribution Shifts in Federated Learning (slides), Distribution Shifts in Data Science Workshop (Seattle, WA, USA), August 2022.
  • Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach (slides), International Conference on Continuous Optimization (ICCOPT) (Bethlehem, PA, USA), July 2022.
  • Federated Learning with Partial Model Personalization (slides), ICML (Spotlight Talk) (Baltimore, MD, USA), July 2022.
  • Federated Learning: Heterogeneity, Robustness, and Optimization. (slides), PhD Defense (University of Washington, Seattle, WA, USA), June 2022.
  • MAUVE: Measuring the Gap Between Neural Text and Human Text (slides), Microsoft Research Asia, March 2022.
  • Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach (slides), INFORMS Optimization Society Conference (Greenville, SC, USA), March 2022.
  • MAUVE: Measuring the Gap Between Neural Text and Human Text (slides), Stanford NLP Seminar, March 2022.
  • MAUVE: Measuring the Gap Between Neural Text and Human Text (slides), NeurIPS Oral Presentation, December 2021.
  • Statistics of Evaluating Generative Models with Divergence Frontier (slides), FAIR Science of Deep Learning, October 2021.
  • A Superquantile Approach to Federated Learning with Heterogeneous Devices, IFDS Ethics and Algorithms, September 2021.
  • Robust Aggregation for Federated Learning, Federated Learning One World Seminar, July 2020.