Tan Minh Nguyen

Coming Soon! 

Assistant Professor (Presidential Young Professor)
Department of Mathematics
National University of Singapore

Email: tanmn@nus.edu.sg
Office: 10 Lower Kent Ridge Road, S17-08-20, Singapore 119076


Ph.D. in Electrical and Computer Engineering, Rice University, 2020
M.S. in Electrical and Computer Engineering, Rice University, 2016
B.S. in Electrical and Computer Engineering, Rice University, 2014

CV, Github, Google Scholar

Brief Biography

I am currently an Assistant Professor of Mathematics (Presidential Young Professor) at the National University of Singapore (NUS). Before joining NUS, I was a postdoctoral scholar in the Department of Mathematics at the University of California, Los Angeles, working with Dr. Stanley J. Osher. I obtained my Ph.D. in Machine Learning from Rice University, where I was advised by Dr. Richard G. Baraniuk. I gave an invited talk in the Deep Learning Theory Workshop at NeurIPS 2018 and organized the 1st Workshop on Integration of Deep Neural Models and Differential Equations at ICLR 2020. I also had two awesome long internships with Amazon AI and NVIDIA Research, during which I worked with Dr. Anima Anandkumar. I am the recipient of the prestigious Computing Innovation Postdoctoral Fellowship (CIFellows) from the Computing Research Association (CRA), the NSF Graduate Research Fellowship, and the IGERT Neuroengineering Traineeship. I received my M.S. and B.S. in Electrical and Computer Engineering from Rice in May 2018 and May 2014, respectively.

I am also writing about simple ideas and principled approaches that lead to working machine learning algorithms on my blog, Almost Convergent.

Research Interests

My research focuses on the interplay of the interpretability, robustness, and efficiency of machine learning models from three principled approaches:

  • Optimization (primal-dual frameworks for deep learning models, momentum-based neural networks, fast multipole transformers)

  • Differential equations (Nesterov neural ordinary differential equations, graph neural diffusion)

  • Statistical modeling (mixture and nonparametric kernel regression frameworks for transformers, deep generative models)


Journal Publications

Conference Publications

Papers on Application

  • Turbulence Forecasting via Neural ODE. NeurIPS Workshop on Machine Learning and the Physical Sciences, 2019.
    Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro, Tan M. Nguyen, Balasubramanya T. Nadiga, Juan A. Saenz, Michael Chertkov, Animesh Garg, Anima Anandkumar, Andreas Dengel, Richard G. Baraniuk, David P. Schmidt.