Tam LE  [Ph.D. 2016]

Tam Le Assistant Professor Department of Advanced Data Science Institute of Statistical Mathematics (ISM) Email: tam[at]ism.ac.jp Research Interests: machine learning, optimal transport, geometric methods, kernels Google Scholar - LinkedIn - GitHub - Research Map

My research aims to make AI more advanced, with a focus on improving its scalability, efficiency, and robustness for learning with large-scale, complex data and models from mathematical perspectives, particularly optimal transport (OT) theory. My current interests are as follows:

1. Deepening OT theory:

   1. Breaking the curse-of-dimensionality for OT with projected 1D-structure.

   2. Going beyond Lp structure for variational Wasserstein problems.

2. Leveraging OT theoretical insights for AI:

   1. Designing innovative, efficient methodologies.

   2. Centering on applications with large-scale, complex structured data.

News

• 27-Jan-2026, Our 2 papers have been accepted to ICLR’26, joint works with Viet-Hoang Tran, Thanh Tran, Thanh Chu, Duy-Tung Pham, Khang Tran, and Tan Nguyen.

  • Tree-sliced Sobolev IPM.

  • Revisiting Tree-Sliced Wasserstein Distance Through the Lens of the Fermat–Weber Problem.

• 20-Nov-2025, I will serve as Area Chair for ICML’26.

• 22-Oct-2025, Our paper, entitled “Tree Structure for the Categorical Wasserstein Weisfeiler-Lehman Graph Kernel” has been accepted to TMLR journal! This is joint work with Keishi Sando, and Hideitsu Hino.

• 19-Sep-2025, Our 2 papers have been accepted to NeurIPS’25.

  • An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph [spotlight], joint work with Truyen Nguyen, Hideitsu Hino, and Kenji Fukumizu.

  • Tree-Sliced Entropy Partial Transport, joint work with Viet-Hoang Tran, Thanh Tran, Thanh Chu, and Tan Nguyen.

• 16-Aug-2025, I will serve as Area Chair for ICLR’26 and AISTATS’26.

• 13-July-2025, I will serve as Senior Program Committee for AAAI’26.

• 01-May-2025, Our 3 papers have been accepted to ICML’25.

  • Tree-Sliced Wasserstein Distance: A Geometric Perspective, joint work with Viet-Hoang Tran, Trang Pham, Tho Tran, Khoi Nguyen, Thanh Chu, and Tan Nguyen.

  • Tree-Sliced Wasserstein Distance with Nonlinear Projection, joint work with Thanh Tran, Viet-Hoang Tran, Thanh Chu, Trang Pham, Laurent El Ghaoui, and Tan Nguyen.

  • Scalable Sobolev IPM for Probability Measures on a Graph, joint work with Truyen Nguyen, Hideitsu Hino, and Kenji Fukumizu.

Vita

I received my Ph.D. in 01/2016 from Kyoto University, supervised by Marco Cuturi and advised by Akihiro Yamamoto. I worked as a postdoc at NITECH & NIMS, between 02/2016 and 08/2017. Then, I worked at RIKEN AIP between 09/2017 and 07/2021 as a researcher; and between 08/2021 and 08/2022 as a research scientist. I have joined Institute of Statistical Mathematics (ISM), Tokyo, in 09/2022, as an assistant professor.

Service

• Area chair: ICML, ICLR, AISTATS

• Editor board reviewer: JMLR

• Program committee: NeurIPS

Contact