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Nguyen Tuan Binh

Nguyen Tuan Binh, PhD

College of Engineering and Computer Science

Assistant Professor, Data Science Program

Biography

Dr. Nguyen Tuan Binh received his PhD in Statistics at the Universite Paris-Sud XI in 2021 and a Master degree in Data Science at Universite Paris-Saclay in 2018. Before that, he got a B.A in Economics at the Foreign Trade University in Vietnam in 2014. Prior to join VinUniversity, he was a Research Fellow at the Mathematics Department of National University of Singapore (2023-2025), a Postdoctoral Researcher at Telecom Paris (2022). His research expertise include large-scale statistical inference, generative modeling and and optimal transport. He is the author of multiple papers at top Machine Learning conferences, including NeuRIPS, ICML, and ICLR.

  • Large-scale statistical inference
  • Conformal Prediction
  • Diffusion/Flow models
  • Test time scaling of Large Language Model
  • Optimal transport with applications in Machine Learning
  • Safety in Frontier Models

  • Probability and Statistics
  • Linear Algebra
  • Optimization
  • Optimal Transport
  • Machine Learning

  1. Chen, JY.*, BN* , Soh, YS. Semidefinite Relaxations of the Gromov-Wasserstein Distance. NeuRIPS 2024. [paper] [code] [reviews] – A preliminary version was presented at the Optimal Transport and Machine Learning workshop at NeuRIPS 2023.
  2. Nguyen, NB., BN, Nguyen, TH., Nguyen, VA. Generative Conditional Distributions byNeural (Entropic) Optimal Transport, ICML 2024. [paper] [code]
  3. Nguyen, , BN, Nguyen, VA. Bellman Optimal Step-size Straightening of Flow-Matching Models, ICLR 2024. [paper & reviews] [code]
  4. Dao, Q., Phung, H., BN, & Tran, A. Flow Matching in Latent Space. arXiv 2023. [paper] [code]
  5. BN, Thirion, B., & Arlot, S. A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension. NeuRIPS 2022. [paper] [code] [reviews]
  6. BN, J.-A. Chevalier, B.Thirion, & S. Arlot, Aggregation of Multiple Knockoffs. In Proceedings of the 37th International Conference on Machine Learning (ICML); PMLR 119:7283-7293, 2020. [paper] [code]
  7. -A. Chevalier, BN, B. Thirion J. Salmon, Spatially relaxed inference on high-dimensional linear models. Statistics & Computing 32, 83 (2022). [paper]

  • 2021: PhD in Statistics, Universite Paris-Sud XI
  • 2018: Msc in Data Science, Universite Paris-Saclay
  • 2014: B.A in Economics, Foreign Trade Univeristy

  • 2022 NeuRIPS Scholar Award

  • https://scholar.google.com/citations?hl=fr&user=6rpHj_YAAAAJ
  • https://tbng.github.io/
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