About me
I am a research engineer at Meta AI (FAIR), working on multimodal models. My research interests include multimodal generation and understanding. I served on the organizing committee for FL-ICML, the program committee for FL-NeurIPS, and as a 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).
Publications (see all)
2024
Byte Latent Transformer: Patches Scale Better Than Tokens
- Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen*, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srinivasan Iyer
- *Joint second author
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