Lam M. Nguyen

I am a Staff Research Scientist at IBM Research, Thomas J. Watson Research Center working in the intersection of Optimization and Machine Learning / Deep Learning. I am also a Principal Investigator of ongoing MIT-IBM Watson AI Lab projects and an IBM Master Inventor. I proposed a new algorithm for machine learning problems called SARAH (which is named after my daughter's name Sarah H. Nguyen) for solving convex and nonconvex large scale optimization problems. This paper is published in The 34th International Conference on Machine Learning (ICML 2017). At IBM Research, my work on "Stochastic Gradient Methods: Theory and Applications" was selected for 2021 IBM Research Accomplishments.

I currently serve as an Action Editor for Journal of Machine Learning Research, Machine Learning, and Neural Networks journals, an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Journal of Optimization Theory and Applications journals, an Area Chair for ICML, NeurIPS, ICLR, CVPR, AAAI, UAI, and AISTATS conferences. I also serve as a Panelist for National Science Foundation (NSF).

I was born in Hanoi, Vietnam, but grew up in Moscow, Russia. I got my Bachelor degree in Applied Mathematics and Computer Science from Faculty (Department) of Computational Mathematics and Cybernetics, Lomonosov Moscow State University in 2008 under the supervision of Prof. Vladimir I. Dmitriev. I also received my M.B.A. degree from McNeese State University, Louisiana in 2013. I got my Ph.D. degree in the Department of Industrial and Systems Engineering at Lehigh University in 2018. I was working with Dr. Katya Scheinberg and Dr. Martin Takáč in the area of Large Scale Optimization for Machine Learning and Stochastic Optimization. During my Ph.D., I was also working with Dr. Alexander Stolyar in the area of Applied Probability, Stochastic Models and Optimal Control. I have won the 2019 P.C. Rossin College of Engineering and Applied Science Elizabeth V. Stout Dissertation Award.

I am very open to collaboration with highly motivated researchers and students. Please feel free to contact me if you would like to have collaborations. Here are my CV and and my personal website.

Fields of interest:

• Design and Analysis of Learning Algorithms
• Optimization for Representation Learning
• Dynamical Systems for Machine Learning
• Federated Learning
• Reinforcement Learning
• Time Series
• Trustworthy / Explainable AI

Academic Services:

• Action Editor / Associate Editor: Journal of Machine Learning Research (2022 - Present), Machine Learning (2021 - Present), Neural Networks (2022 - Present), IEEE Transactions on Neural Networks and Learning Systems (2022 - Present), Journal of Optimization Theory and Applications (2022 - Present).
• Area Chair / Meta-Reviewer / Senior Program Committee: ICML (2020, 2021, 2022, 2023), NeurIPS (2022, 2023), ICLR (2021, 2022, 2023), AISTATS (2021, 2022, 2023), CVPR (2023), UAI (2022, 2023), AAAI (2022).
• Organizing Committee: Journal Chair (NeurIPS 2023), Workshop Organizer (NFFL NeurIPS 2021, MLmDS AAAI 2023).
• Grant Reviewer: National Science Foundation, AI Singapore Research Programme.
• Reviewer / Program Committee: ICML, NIPS/NeurIPS, ICLR, AISTATS, COLT, UAI, AAAI, IJCAI, CVPR, ICCV, ECCV.
• Reviewer: Journal of Machine Learning Research, Mathematical Programming, SIAM Journal on Optimization, SIAM Journal on Numerical Analysis, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Signal Processing, Artificial Intelligence, Optimization Methods and Software, SIAM Journal on Mathematics of Data Science.

Selected Publications:

• Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takac. 'SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient'. The 34th International Conference on Machine Learning (ICML 2017).
• Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtarik, Katya Scheinberg, Martin Takac. 'SGD and Hogwild! Convergence Without the Bounded Gradients Assumption'. The 35th International Conference on Machine Learning (ICML 2018).
• Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk. 'A Unified Convergence Analysis for Shuffling-Type Gradient Methods'. The Journal of Machine Learning Research 2021.

Full list of publications

Honors & Awards:

• IBM 8th Plateau Invention Achievement Award, 2023
• IBM Master Inventor, 2022
• IBM 7th Plateau Invention Achievement Award, 2022
• IBM 6th Plateau Invention Achievement Award, 2022
• IBM Outstanding Technical Achievement Award, 2022
• IBM 5th Plateau Invention Achievement Award, 2022
• IBM 4th Plateau Invention Achievement Award, 2022
• IBM 3rd Plateau Invention Achievement Award, 2021
• IBM 2nd Plateau Invention Achievement Award, 2020
• IBM Research Division Award, 2020
• IBM Outstanding Technical Achievement Award, 2020
• IBM 1st Plateau Invention Achievement Award, 2020
• NeurIPS 2019 Top Reviewers, 2019
• Elizabeth V. Stout Dissertation Award, Lehigh University, PA, USA, 2019
• Van Hoesen Family Best Publication Award, Lehigh University, PA, USA, 2018
• Dean’s Doctoral Fellowship (RCEAS), Lehigh University, PA, USA, 2016 - 2017
• Beta Gamma Sigma (Academic Honor), McNeese State University, LA, USA, 2014

IBM Research Accomplishments:

• Federated Learning Security and Privacy (O-level), 2022
• Dynamic Approaches for Machine Learning (A-level), 2022
• Regression Optimization for Heavy Processing Industries (A-level), 2022
• Combinatorial Sparsity for AI (A-level), 2022
• Stochastic Gradient Methods: Theory and Applications (A-level), 2021
• SROM: Smarter Resource & Operations Management (A-level), 2019

MIT-IBM Projects:

• Principal Investigator, "Safe Learning for Time Series Problems: Data, Structure and Optimization": 01/2023 - 12/2025
• Principal Investigator, "Safe AI Certification": 01/2022 - 12/2022
• Principal Investigator, "Safety Structures, Certification, and Training for AI in the Feedback Loop": 01/2021 - 12/2021
• Co-Principal Investigator, "Hierarchical Disentangled Representations for Scalable Multi-agent Reinforcement Learning": 09/2020 - 09/2021

IBM Activities:

• Champion, International Conference on Machine Learning (ICML): 01/2022 - Present
• Member, Invention Development Team (IDT): 11/2021 - Present
• Champion, Professional Interest Community (PIC) - Learning: 07/2021 - Present
• Member, Research AI Pillar Accomplishment Committee: 2022
• Reviewer, 2021 Pat Goldberg Memorial Best Paper Competition: 2022
• Reviewer, IBM Ph.D. Fellowships: 2020