# Thanh Nguyen-Tang

Postdoctoral Researcher
Applied AI Institute (A$$^2$$I$$^2$$)
Deakin University, Australia
Email: nguyent2792 [AT] gmail [DOC] com

I am currently a postdoctoral researcher of the Applied AI Institute at Deakin University. Prior to that, I was a PhD student at Deakin University from Jan 2019 to Jul 2021, was a researcher at Ulsan National University of Science and Technology (UNIST) from Mar 2018 to Dec 2019, and obtained my Master at UNIST in 2018.

I work on machine learing with the goal of developing advanced data-driven machine learning and statistical methods for learning and reasoning in complex, structured models over large, structured datasets. In particular, I am interested in improving statistical efficiency and generalization beyond i.i.d. setting of machine and reinforcement learning systems via imposing/exploiting minimal structural inductive biases. Currently, I focus on: (i) (offline) RL theory (with neural function approximation), and (ii) robust generalization beyond i.i.d. setting, (iii) ML applications. See more at my research proposals.

I publish under Thanh Tang Nguyen (old name arrangement) and Thanh Nguyen-Tang (new one). Here are my latest CV, Google Scholar, Semantic Scholar, Github, Twitter .

## Latest News

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

• [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%.

## Services

• Invited Reviewer/ Program Committee: NeurIPS’21 Workshop on Offline Reinforcement Learning, ICML (2021), NeurIPS (2020, 2021), ICLR (2021 - outstanding reviewer award, 2022), AAAI (2021, 2022), AISTATS (2021).