- Robust Aggregation for Federated Learning (slides recording), IEEE Webinar, June 2024.
- Towards User-Level Differential Privacy at Scale (slides), IIT Hyderabad, April 2024.
- Correlated Noise Provably Beats Independent Noise for Differentially Private Learning (slides), Laboratoire Jean Kuntzmann, UGA (Grenoble, France), February 2024.
- Towards User-Level Differential Privacy at Scale (slides), Google Federated Learning Seminar (Postdoc final presentation), February 2024.
- Unleashing the Power of Randomization in Auditing Differentially Private ML, GraPFiCs Workshop at UC Santa Cruz, October 2023.
- MAUVE Scores for Generative Models, ETH Zürich (Zurich, Switzerland), June 2023.
- Distributionally Robust Federated Learning: Differential Privacy and Fast Optimization, EPFL (Lausanne, Switzerland), June 2023.
- Distributionally Robust Federated Learning with Differential Privacy (slides), SIAM Conference on Optimization (OP23) (Seattle, WA, USA), May 2023.
- Towards Next-Generation ML/AI: Robustness, Optimization, Privacy (slides), Faculty Job Talk (Multiple locations), January 2023.
- Tackling Distribution Shifts in Federated Learning with Superquantile Aggregation (slides), NeurIPS DistShift (Spotlight Talk) (New Orleans, LA, USA), December 2022.
- Federated Learning with Partial Model Personalization (slides), Federated Learning One World Seminar, October 2022.
- MAUVE: Measuring the Gap Between Neural Text and Human Text (slides), IFML/NSF Site Visit, August 2022.
- Tackling Distribution Shifts in Federated Learning (slides), IFDS Workshop on Distributional Robustness in Data Science (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, FL-ICML Long Presentation, July 2020.
- Robust Aggregation for Federated Learning, Federated Learning One World Seminar, July 2020.
- A Smoother Way to Train Structured Prediction Models, Facebook AI Research, July 2019.
