Tam Le (RIKEN AIP)
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(a.k.a. Lê Thanh Tâm) |
Research interests: Optimal transport, tree-Wasserstein geometry, geometric machine learning, Riemannian manifold, topological data analysis, kernel methods, parametric optimization, metric learning.
Contact
RIKEN AIP (Kyoto University Office)
Artificial Intelligence Research Unit
Graduate School of Informatics, Kyoto University
Yoshida Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Email: tam.le[at]riken.jp or lttam.vn[at]gmail.com
My CV is here - Updated April, 2020.
News
05-Apr-2020, Our proposal has been accepted by the Grants-in-Aid for Young Scientists as PI (04/2020 - 03/2023).
26-Oct-2019, Matlab code for tree-(sliced)-Wasserstein distance in our NeurIPS’19 (Tree-Sliced Variants of Wasserstein Distances) is available [Github].
11-Oct-2019, Matlab code for Kmeans with Flow-based Tree Gromov-Wasserstein in “Computationally Efficient Tree Variants of Gromov-Wasserstein” is available [Github].
04-Sep-2019, Our paper, entitled “Tree-Sliced Variants of Wasserstein Distances” has been accepted to NeurIPS’19! This is joint work with Marco Cuturi, Kenji Fukumizu, and Makoto Yamada.
11-Jul-2019, Slides for my presentation (Kernel methods for Persistence Diagrams: A Geometric Approach) at Institute for Advanced Study, Kyoto University.
25-Apr-2019, Python code for our ICML’19 (Safe Grid Search with Optimal Complexity) is available [Github], coded by Eugene Ndiaye.
22-Apr-2019, Our paper, entitled “Safe Grid Search with Optimal Complexity” has been accepted to ICML’19. This is joint work with Eugene Ndiaye, Olivier Fercoq, Joseph Salmon and Ichiro Takeuchi.
19-Oct-2018, Matlab code v0.1 for Persistence Fisher distance (Fisher information metric between two persistence diagrams with or without Fast Gauss Transform) is available [Github].
05-Sep-2018, Our paper, entitled “Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams” has been accepted to NeurIPS’18!
01-Sep-2017, Joined in High-Dimensional Statistical Modeling Team, RIKEN AIP as a postdoctoral researcher.
05-Apr-2017, Our proposal has been accepted by the Grants-in-Aid for Young Scientists (B) as PI (04/2017 - 03/2020).
Research Support
04/2020 - 03/2023, JSPS KAKENHI Grant number 20K19873, Grants-in-Aid for Young Scientists as PI (4.2M JPY).
04/2017 - 03/2020, JSPS KAKENHI Grant number 17K1274, Grants-in-Aid for Young Scientists (B) as PI (4.1M JPY).
Education
10/2012 - 09/2015, PhD program, Graduate school of Informatics, Kyoto University, Japan.
Supervisors: Professor Marco Cuturi, Professor Akihiro Yamamoto.
I completed my PhD program in September, 2015 and officially received my PhD degree in January, 2016.
Research Experience
09/2017 - present, Postdoctoral Researcher, High-Dimensional Statistical Modeling Team, RIKEN Center for Advanced Intelligence Project (RIKEN AIP).
Mentor: Professor Makoto Yamada
02/2016 - 08/2017, Postdoctoral Researcher, Nagoya Institute of Technology, Japan & National Institute for Materials Science, Japan.
Mentor: Professor Ichiro Takeuchi.
01/2016, Associate Researcher, Nagoya Institute of Technology, Japan.
Mentor: Professor Ichiro Takeuchi.
10/2015 - 12/2015, Associate Researcher, Kyoto University, Japan.
Mentors: Professor Akihiro Yamamoto, Professor Marco Cuturi.
Research Intern Experience
01/2014 - 02/2014, Internship student, Lear, INRIA, Grenoble, France.
Supervisor: Professor Zaid Harchaoui.
03/2013, Internship student at Lear, INRIA, Grenoble, France.
Supervisor: Professor Zaid Harchaoui.
08/2010 - 02/2011, Internship student, National Institute of Informatics, Tokyo, Japan.
Supervisor: Professor Akihiro Sugimoto
04/2009 - 06/2009, Internship student, Toyota Technological Institute of Nagoya, Japan.
Supervisor: Professor Seiichi Mita