Tam Le (PhD, 2016)

Assistant Professor, The Institute of Statistical Mathematics (ISM) Department of Advanced Data Science Research Center for Statistical Machine Learning Concurrent affiliation: Visiting Scientist, Functional Analytic Learning Team, RIKEN Center for Advanced Intelligence Project (RIKEN AIP). Assistant Professor, The Graduate School for Advanced Studies (SOKENDAI).

Research: Machine Learning, Optimal Transport

I have been working on theory, methodology, and application of learning with large-scale, complex data and models from mathematical perspectives. More concretely, my recent research includes:

• Theory: optimal transport theory for measures with graph structure, with functional geometric structure; and for variational Wasserstein problems.

• Methodology: scalability, robustness, and efficiency of optimal transport for learning with measures in machine learning and deep learning, including deep generative model, diffusion model, data valuation for LLM pretraining.

• Application: for domains with large-scale, complex structure data, including non-Euclidean, high-dimension, dynamic, nonlinearity, corruption.

I would love to expand my research spectrum to more interesting and impactful (related) research problems.

Contact

My CV is here - Updated March, 2025.

News

• 25-Mar-2025, Our paper, entitled “Few-Shot Object Detection via Synthetic Features with Optimal Transport” has been accepted to Computer Vision and Image Understanding (CVIU) journal! This is joint work with Khoa Nguyen, Toan Do, Tiep Nguyen, Triet Tran, and Tam V. Nguyen.

• 23-Jan-2025, Our 3 papers have been accepted to ICLR’25.

  • SAVA: Scalable Learning-Agnostic Data Valuation, joint work with Samuel Kessler, and Vu Nguyen.

  • Distance-Based Tree-Sliced Wasserstein Distance, joint work with Hoang Tran, Khoi Nguyen, Trang Pham, Thanh Chu, and Tan Nguyen.

  • Spherical Tree-Sliced Wasserstein Distance, joint work with Hoang Tran, Thanh Chu, Khoi Nguyen, Trang Pham, and Tan Nguyen.

• 22-Nov-2024, I will serve as Area Chair for ICML’25.

• 11-Sep-2024, I will serve as Area Chair for AISTATS’25.

• 07-Aug-2024, I will serve as Area Chair for ICLR’25, and as Senior Program Committee for AAAI’25.

• 02-May-2024, Our 2 papers have been accepted to ICML’24.

  • Sliced Wasserstein with Random-Path Projecting Directions, joint work with Khai Nguyen, Shujian Zhang, and Nhat Ho.

  • Generalized Sobolev Transport for Probability Measures on a Graph, joint work with Truyen Nguyen, and Kenji Fukumizu.

• 20-Jan-2024, Our paper, entitled “Optimal Transport for Measures with Noisy Tree Metric” has been accepted to AISTATS’24! This is joint work with Truyen Nguyen and Kenji Fukumizu.

• 01-Dec-2022, I am officially appointed as a visiting scientist at Functional Analytic Learning Unit, RIKEN Center for Advanced Intelligence Project (RIKEN AIP).

• 01-Sep-2022, I officially join in The Institute of Statistical Mathematics (ISM) as Assistant Professor. I am also an Assistant Professor at The Graduate School for Advanced Studies (SOKENDAI).

Vita

I received my Ph.D in 01/2016 from Kyoto University, Japan, supervised by Marco Cuturi and Akihiro Yamamoto. I worked as a post-doctoral researcher at Nagoya Institute of Technology & National Institute for Materials Science, Japan, mentored by Ichiro Takeuchi between 02/2016 and 08/2017. I then worked as a post-doctoral researcher between 09/2017 and 07/2021, and later as a research scientist between 08/2021 and 08/2022 at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), mentored by Makoto Yamada. From 09/2022, I have been working as an assistant professor at The Institute of Statistical Mathematics (ISM), and I have concurrently been a visiting scientist at RIKEN AIP, working in Functional Analytic Learning Team led by Minh Ha Quang from 12/2022.

Selected Service

• Area chair: ICML, ICLR, AISTATS

• Program committee: NeurIPS

• Reviewer: JMLR