I am an assistant professor in the Department of Data Science, Ying Wu College of Computing at New Jersey Institute of Technology (NJIT). Prior to that, I was a postdoc at Johns Hopkins University (with Raman Arora), did my PhD in Computer Science at Deakin University, Australia, my M.Sc. in Computer Science and Engineering at Ulsan National Institute of Science and Technology, South Korea, my B.Eng. in Electronic and Communication Engineering (Talented Engineering Program) at Danang University of Science and Technology, Vietnam. I was awarded the Alfred Deakin Medal for Doctoral Theses, 2022.

Postdoctoral Research Fellow ; John Hopkins University ; Computer Science ; 2025

Ph.D. ; Deakin University ; Computer Science ; 2022

M.S. ; Ulsan National Institute of Science and Technology ; Computer Science and Engineering ; 2018

https://thanhnguyentang.github.io/

CS 669 - REINFORCEMENT LEARNING

I am teaching machine learning (theory) and reinforcement learning (theory).

I am mostly interested in the theoretical and algorithmic aspects of machine learning motivated by real-world problem settings. I like to seek a mathematical understanding of the underlying algorithmic principles for learning with strong adaptivity to problem structures, and thereby design efficient machine learning algorithms with strong theoretical guarantees. The current topics include, but not limited to, reinforcement learning (e.g., Offline RL, Multi-agent, Multi-task), theory of reasoning with transformers, and responsible AI (e.g., privacy, robustness, unlearning). I direct the Statistical and Algorithmic ML Lab @ NJIT.

Journal of Machine Learning Research
Peer Reviewer, Journal , 2025