I am an MPhil candidate working with Lexing Xie and Colin Klein at the Computational Media Lab, the Australian National University. I am also affiliated with the Humanising Machine Intelligence Grand Challenge at ANU.

My thesis is in computational social science. I am interested in real-life moral dilemmas, social dynamics and, more broadly, practical ethics. My research aims to study relevant large-scale social phenomena using computational methods grounded in machine learning, moral psychology and natural language processing.

I also have a special interest in optimization, especially in the context of machine learning. In 2019 and 2020, I worked as an intern with Nguyen Tran at the School of Computer Science, the University of Sydney, on designing personalized and communication-efficient algorithms for federated learning.

Previously I graduated with a BSc in computer science from the School of Computing and Information Systems, the University of Melbourne. During my undergraduate studies, I worked with Charl Ras at the School of Mathematics and Statistics on designing resilient network embeddings.

I publish under my official Vietnamese name, Tuan Dung Nguyen.


[10-11-2022] I made a YouTube channel! The first video is a lecture on optimal transport I gave to the Advanced ML class at ANU. The slides are here.

[12-05-2022] I gave a talk on our ICWSM 2022 paper at the ANU’s AI, ML and Friends seminar. The slides can be found here. There’s also a press coverage by ANU here.

[16-03-2022] Our paper, Mapping Topics in 100,000 Real-life Moral Dilemmas, is accepted to ICWSM 2022! The code can be found here and data here.

[30-11-2021] I am honored to attend the International School in Artificial Intelligence and its Applications in Computer Science (ISAAC 2021) this december at Monash University.

[27-04-2021] I will be volunteering at the ICWSM-2021 conference.

[19-04-2021] I will be volunteering at the AIES-2021 conference. Thanks HMI for your invitation.

[16-04-2021] I am honored to be selected to the Cornell, Maryland and Max Planck Pre-doctoral Research School (CMMRS-21).

[15-12-2020] Check out our pre-print DONE: Distributed Newton-type Method for Federated Edge Learning on arXiv.

[26-10-2020] Our work, titled Personalized Federated Learning with Moreau Envelopes, is accepted to NeurIPS 2020. The code can be found here.

[26-05-2020] Our paper, Federated Learning with Proximal Stochastic Variance Reduced Gradient Algorithms, is accepted to ICPP 2020.