About me
I am a research engineer at Meta AI (FAIR), working AI guided design, 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)
2024
Now It Sounds Like You: Learning Personalized Vocabulary On Device
- Sid Wang, Ashish Shenoy, Pierce Chuang, John Nguyen
- AAAI 2024 Spring Symposium
2023
READ: Recurrent Adaptation of Large Transformers
- John Nguyen*, Sid Wang*, Ke Li, Carole-Jean Wu
- NeurIPS 2023 R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Foundation Models Workshop
On Noisy Evaluation in Federated Hyperparameter Tuning
- Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
- Conference on Machine Learning and Systems (MLSys), 2023.
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning
- John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael Rabbat
- Spotlight at International Conference on Learning Representations (ICLR) 2023
- Presentation
2022
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.
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.
Conference on Machine Learning and Systems (MLSys), 2022.
Federated Learning with Buffered Asynchronous Aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba.
- International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
- Presentation
2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
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.