tnt


Thanh Nguyen-Tang

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

Intro. I am currently an Associate Research Fellow and have recently finished my PhD (Feb 2022) at the Applied AI Institute, Deakin University. Prior to that, I was a researcher at Ulsan National University of Science and Technology (UNIST) from Mar 2018 to Dec 2018, and I obtained my Master at UNIST in 2018. I was also serving as a ML technical consultant for a local startup company on NLP product solutions.

Research interests. Algorithmic and theoretical foundations of modern machine learning - reinforcement learning, deep learning and representation learning.

We are running a mini Modern Statistical Learning and Optimization Seminar (MSLOS).

Iā€™m always actively open to research collaborations and chat!

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

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%.

Publications

2022

2021

2020

2019

Dissertations

Supervision and mentoring

  • Ragja Palakkadavath – Ph.D. student at A2I2, Deakin University ā€“ Topic: Domain Generalization

  • Nguyen Ngoc Hieu – Resident at FPT.AI – Topic: Learning Theory for Implicit Deep Models.

  • Qiyao Wei, senior undergrad at University of Toronto ā€“ Topic: Research statement in offline RL for PhD application

Academic Service

  • Reviewer, NeurIPS, 2022

  • Program Committee, EWRL, 2022

  • Reviewer, L4DC, 2022 (1 paper)

  • Reviewer, ICML, 2022 (3 papers)

  • 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

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.