About me

I am a research engineer working on large multimodal models in Luke Zettlemoyer’s group at Meta AI (FAIR). My research interests include multimodal generation, understanding, and open world perception. My goal is to understand and build a unified world model that is visualy intelligent. I previously worked on federated learning at Meta.

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

Toward Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity

  • 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