Thanh Nguyen-Tang

Associate Research Fellow
Applied AI Institute (A\(^2\)I\(^2\))
Deakin University, Australia
Email: nguyent2792 [AT] gmail [DOC] com

I am currently an Associate Research Fellow and was a PhD candidate of the Applied AI Institute at Deakin University. Prior to that, I was a researcher at Ulsan National University of Science and Technology (UNIST) from Mar 2018 to Dec 2019, and I obtained my Master at UNIST in 2018.

The goal of my research is to develop better understanding and tractable methods for learning and decision-making in modern practical settings with strong theoretical guarantees. My research interest lies at the intersection of machine learning with statistics and optimization. My research areas:

  • Sequential decision-making under uncertainty (e.g., reinforcement learning, bandit, online learning),

  • Robust generalization (e.g. OOD, adversarial learning)

  • Statistical inference (e.g. semiparametric models and causal inference)

Our Institute at Deakin University have several PhD openings in offline learning and causal inference. Please contact me for more information.

Here are my latest CV, Google Scholar, Semantic Scholar, Github, Twitter .

I created the “ML Theory Exchange Network” Discord channel (currently 66 members as of 25 Oct 2021) to connect ML-theory passionate self-learners (like myself) with senior researchers for exchanging ideas and learning resources.

I have been also serving as a ML technical consultant for a local startup company on NLP product solutions.

Latest News

  • [Jan 21, 2022] One paper got accepted to ICLR, 2022.

  • [Oct 25, 2021] A short version of our work has been accepted to the NeurIPS’21 Workshop on Offline Reinforcement Learning.

  • [Jul 8, 2021] A short version of our work has been accepted to the ICML’21 Workshop on Reinforcement Learning Theory.

  • [Jul 1, 2021] I start my postdoc at A\(^2\)I\(^2\), Deakin University after submitting my Ph.D. thesis in 24 Jun.

  • [May 20, 2021] I have been accepted to the Deep Learning Theory Summer School at Princeton, acceptance rate: 180/500 = 36%.




Supervision and mentoring

  • Ragja Palakkadavath, Ph.D. student in ML at A2I2, Deakin University – Topic: Domain Generalization – Role: Associate supervisor

  • Qiyao Wei, senior undergrad at University of Toronto – Role: PhD application mentor

Academic Service

  • Reviewer, L4DC, 2022

  • Reviewer, ICML, 2022

  • Program Committee, NeurIPS Workshop on Offline Reinforcement Learning, 2021

  • Program Committee, AAAI, 2022

  • Reviewer, ICLR, 2022

  • Reviewer, NeurIPS, 2021

  • Reviewer, ICML, 2021

  • Reviewer, AISTATS, 2021

  • Program Committee, AAAI, 2021

  • Reviewer, ICLR, 2021 (outstanding reviewer award)

  • Reviewer, NeurIPS, 2020