About me

I am a senior research engineer at Meta AI Research, working with Ilya Mironov and Mike Rabbat. My research interests include large-scale machine learning, federated and on-device learning, and differential privacy. I served as technical program committee for FL-ICML, and reviewer for ICML, NeurIPS, AISTATS and MLSys.

Prior to Meta, I received my Masters from UC Davis in 2019, where I worked with Prem Devanbu and Vincent Hellendoorn as a member of the empirical software engineering lab (DECAL).

Publications (see all)

Preprints

Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning

  • John Nguyen, Kshitiz Malik, Maziar Sanjabi, Michael Rabbat.

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.

  • 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.

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.