I am a Research Software Engineer at Google Research based in New York. I completed by B.S. in Mathematics-Computer Science at the University of California, San Diego (UCSD) where I was advised by Garrison W. Cottrell. My co-advisors and cross-collaborators include Xiaoqian Jiang, Shuang Wang, David Meyer, and Bradley Voytek.

Research

My current research interests include algorithmic and theoretically scoped areas motivated by real-world problems, focusing on algorithmic fairness and ML/AI ethics, AI for climate change and weather, and federated learning.

Publications

1. FedBoost: Communication-Efficient Algorithms for Federated Learning, ICML 2020. with A.T. Suresh and M. Mohri [pdf]

2. SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2018. with Y. Jiang, C. Wang, et al. [pdf]

3. The "Day in the Life" Project: A Preliminary Report. Journal of Vision 2019. with G.W. Cottrell and C. Gonzalez

Talks and Presentations

1. “Federated Learning for All: Crowdsourced Machine Learning on its Journey to Privacy & Fairnes”. ACM Richard Tapia Celebration of Diversity in Computing Conference, 2020 (peer-reviewed technical workshop).

2. “Communication-Efficient Ensemble Methods for Federated Learning”. New York Academy of Sciences, 14th Annual Machine Learning Symposium, 2020 (peer-reviewed spotlight talk and poster). with A.T. Suresh and M. Mohri

3. “Applying Deep Neural Networks to Test Human Perceptual Biases in Spoken Language”. Chancellor’s Research Excellence Scholarship Symposium at UC San Diego, 2018. with G.W. Cottrell

4. “Day In The Life: A Human-Centric Approach to Modeling the Visual System”. SACNAS National Diversity in STEM Conference, 2018 (peer reviewed). with C. Gonzalez and G.W. Cottrell

5. "Modeling the Human Visual System". Invited talk at the Perceptual Expertise Network bi-annual meeting (co-located with the Conference on Cognitive Computational Neuroscience [CCN]), 2018. with G. W. Cottrell.