I am a research engineer at Meta AI (FAIR), working on AI guided hardware designs, federated learning and privacy in machine learning. My research interests include federated learning, training efficiency of large-scale generative AI systems, and privacy-preserving and robust machine learning. I served on the organizing committee for FL-ICML, the program committee for FL-NeurIPS, and reviewer for ICLR, ICML, NeurIPS, AISTATS, and MLSys.
Before Meta, I graduated Cum Laude from UC Davis with double majors in Statistics and Computer Science (2018) and M.S. in Computer Science (2019). At UC Davis, I worked with Prem Devanbu and Vincent Hellendoorn on empirical software engineering.
Publications (see all)
- Sid Wang, John Nguyen, Ke Li, Carole-Jean Wu
- Sid Wang, Ashish Shenoy, Pierce Chuang, John Nguyen
- Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
- Conference on Machine Learning and Systems (MLSys), 2023.
- John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael Rabbat
- Spotlight at International Conference on Learning Representations (ICLR) 2023
Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu.
Best Paper Finalist Award at the ACM Conference Series on Recommender Systems (RecSys), 2022.
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.
Conference on Machine Learning and Systems (MLSys), 2022.
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba.
- International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
Ashkan Yousefpour*, Igor Shilov*, Alexandre Sablayrolles*, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, Ilya Mironov. ∗Equal contribution.
Privacy in Machine Learning (PriML) workshop, NeurIPS 2021.