John Nguyen

RESEARCH SCIENTIST | NEW YORK CITY, UNITED STATES

John Nguyen is a research engineer at Meta AI (FAIR), working on AI guided designs, federated learning and privacy in machine learning. His research interests include federated learning, training efficiency of large-scale generative AI systems, and privacy-preserving and robust machine learning. a research engineer at Meta AI (FAIR), working on AI guided hardware designs, federated learning and privacy in machine learning. His research interests include federated learning, training efficiency of large-scale generative AI systems, and privacy-preserving and robust machine learning. He served on the organizing committee for FL-ICML, the program committee for FL-NeurIPS, and reviewer for ICLR, ICML, NeurIPS, AISTATS, and MLSys. He graduated Cum Laude from UC Davis with double majors in Statistics and Computer Science (2018) and M.S. in Computer Science (2019).

John's Publications

August 08, 2022

RESEARCH

CORE MACHINE LEARNING

Opacus: User-Friendly Differential Privacy Library in PyTorch

Ashkan Yousefpour, Akash Bharadwaj, Alex Sablayrolles, Graham Cormode, Igor Shilov, Ilya Mironov, Jessica Zhao, John Nguyen, Karthik Prasad, Mani Malek, Sayan Ghosh

August 08, 2022

April 26, 2022

PAPAYA: PRACTICAL, PRIVATE, AND SCALABLE FEDERATED LEARNING

Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek

April 26, 2022

September 14, 2021

Federated Learning with Buffered Asynchronous Aggregation

John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba

September 14, 2021