Tam Le (PhD, 2016)

Assistant Professor, The Institute of Statistical Mathematics (ISM) Department of Advanced Data Science Research Center for Statistical Machine Learning Email: tam[at]ism.ac.jp CV - Google scholar - GitHub - Research map

Research Interests: Machine Learning, Optimal Transport, Kernels

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 optimal transport (OT) theory perspectives. More specifically, my research objectives are as follows:

1. Deepening OT theory:

   1. Breaking the curse of dimensionality for OT with projected 1D-structure, e.g., graph structure, tree system, tree structure.

   2. Going beyond Lp-structure for variational Wasserstein problems, e.g., functional geometric structure.

2. Leveraging OT theoretical insights for data science:

   1. Designing innovative, and efficient methodologies, e.g., for data valuation, LLM pretraining, diffusion model, generative model.

   2. Centering on applications with large-scale, complex structured data, e.g., non-Euclidean, dynamic, nonlinearity, corruption.

Contact

News

• 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 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, 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.

• 25-Mar-2025, Our paper, entitled “Few-Shot Object Detection via Synthetic Features with Optimal Transport” has been accepted to 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.

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 post-doctoral researcher at NITECH & NIMS, between 02/2016 and 08/2017. Then, I worked at RIKEN AIP between 09/2017 and 07/2021 as a post-doctoral researcher; and between 08/2021 and 08/2022 as a research scientist. I have joined The Institute of Statistical Mathematics (ISM), Tokyo, in 09/2022, as an assistant professor.

Selected Service

• Area chair: ICML, ICLR, AISTATS

• Editor board reviewer: JMLR

• Program committee: NeurIPS