Tam Le (PhD, 2016)

Tam Le 

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 AIP.
Assistant Professor, The Graduate School for Advanced Studies (SOKENDAI).

Research: Machine Learning, Optimal Transport

My research aims to improve scalability, efficiency, and robustness for learning with large-scale, complex data and models from optimal transport (OT) theory perspectives, with a focus on two objectives:

  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 data science:

    1. Designing innovative, and efficient methodologies.

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

Contact

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Email: tam[at]ism.ac.jp (or lttam.vn[at]gmail.com)
Room: A506B. Access to ISM

My CV is here - Updated March, 2025.

News

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