Tuan-Anh Bui

Machine Learning Researcher in Generative AI and Trustworthy Machine Learning

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Me and two boys

I am Research Fellow at the Department of Data Science and AI, Monash University. My research interest lies in the intersection between Generative AI and Trustworthy Machine Learning. For example, my research focuses on how to ensure that models like ChatGPT do not respond to harmful queries asking to create a bomb, or that models like Stable Diffusion do not generate sexual images. I got my Ph.D. from Monash University in November 2023, under the supervision of Prof. Dinh Phung and Dr. Trung Le. My thesis can be found here.

Before that, I had one year working as Research Engineer at Credit AI Lab, Trusting Social, and two years working as Research Assistant at Singapore University of Technology and Design with Prof. Ngai Man Cheung and Dr. Trung Tran.

news

Sep 25, 2025 I am excited to share that I have been invited to apply for the National Innovation (Global Talent) visa by the Australian Government. It is a great honor to me, as the scheme is very competitive as only less than 5% EOI get invited (130 invitations out of 6400 EOIs). I am looking forward to the opportunities this visa will bring and hope to be able to contribute meaningful to the AI community in Australia.
Jul 19, 2025 It was a great pleasure for me to be one of 200 young Vietnamese intellectuals selected to participate in the 6th Global Young Vietnamese Intellectuals Forum (Global YVI Forum) in Hanoi, Vietnam. The forum is a platform for young Vietnamese intellectuals to contribute their thoughts and ideas to the development of Vietnam, focusing on the themes of Science, Technology, Innovation and Entrepreneurship.
May 20, 2025 Proud to be selected as a Notable Reviewer at ICLR 2025! The award is given to those who reviewed four or more papers. In my case, I put considerable effort not only into the initial reviews but also into engaging in discussions with the authors — all with one goal in mind: identifying which aspects of the papers needed to be included or improved. It was neither an easy nor a quick task, requiring several hours just to read and understand each paper, followed by extensive effort during the rebuttal phase (one paper involved a total of 14 back-and-forth exchanges). This all happened while I was also busy in the role of an author myself. In the end, I raised the ratings of 3 out of the 4 papers I reviewed and supported their acceptance and they were indeed accepted! :D I’m glad to see that my efforts were recognized.
Mar 21, 2025 It was a great pleasure for me to present our works about Unlearning Concepts to the Machine Learning team at Canva. The slides are available here. I’m glad to see many interesting and practical/industry-related questions from the audience and see how our research can be applied to their real-world problems.
Feb 28, 2025 I’m excited to share that I am officially a Chief Investigator of the Trustworthy Generative AI: Towards Safe and Aligned Foundation Models project, funded by the Department of Defence, Australia with an $800K AUD grant. The project focuses on four key areas of modern foundation models: Certification - Alignment - Multimodality - Personalization, where I am leading the Personalization stream. Our goal is to push the boundaries of safe and aligned generative AI, ensuring its responsible deployment in real-world applications. The project is led by Professor Dinh Phung and co-led by a team of experts from the Faculty of IT, Monash University, where I am honored to be part of.
Feb 27, 2025 I’m excited to share that our paper “Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation” (led by Long Vuong) has been accepted to CVPR 2025! :fire::fire::fire: While CLIP-based methods for Unsupervised Domain Adaptation (UDA) have shown promise, they face limitations in target domain generalization due to embedding distribution shifts. In this paper, we propose a novel approach that exploits the geometric relationships between visual and text embeddings through optimal transport theory. By leveraging clustering behavior in multi-modal embeddings and reference predictions from source prompts, our method achieves superior performance in target-prompt learning and representation quality.
Jan 23, 2025 Hooray! I’m thrilled to finally share that our work has been accepted to ICLR 2025! This is more than just an acceptance—I’m truly proud that all reviewers recognized and appreciated the originality and creativity of our approach to concept unlearning, with a clear motivation and comprehensive experiments. The paper can be found here :fire::fire::fire:
Oct 4, 2024 Excited to share another paper that I am very proud of. This paper is an extension of our NeurIPS 2024 paper, where we dive deeper into the impact of erasing one concept to the others, but this time, we focus on the choice of target concepts. The paper can be found here. Our paper’s name was inspired by the movie “Fantastic Beasts and Where to Find Them”. Hopefully, the reviewers enjoy it as much as the movie :joy:.
Sep 26, 2024 Proudly to share that our paper “Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation” has been accepted at NeurIPS 2024. We had a challenging rebuttal period, where we worked hard to address the feedback from some tough but silent reviewers. Fortunately, we had other reviewers who actively engaged with us, sought to understand our paper, and ultimately championed it. So, in this happy moment, I want to express my gratitude to the anonymous reviewers ❤️, as well as to my incredible collaborators from Monash and DST. We will soon update the paper with all the details and code. The paper can be found here with its slides. Hope you enjoy it.
Jun 28, 2024 I am thrilled and proud to see the Trustworthy Machine Learning project, on which I have been a key contributor since my PhD, being extended to a new 3-year project funded by the Department of Defense, Australia. The project will focus on various aspects of Trustworthy Generative Models, including alignment, safety, and robustness. This project is not only the first major grant on Generative AI in our DSAI department but also across the entire FIT at Monash University. 🎉 🎉 🎉
Nov 1, 2023 I officially become a Dr. today! My thesis “Enhancing Adversarial Robustness: Representation, Ensemble, And Distribution Approaches” is available here. Today is also my wedding anniversary :joy: Hooray!
Sep 22, 2023 Our paper “Optimal Transport Model Distributional Robustness” has been accepted to NeurIPS 2023! 🎉 (led by Van-Anh Nguyen)
Jun 24, 2023 Presenting “Exploring Controllability of Conditioned Diffusion Models” at prof. Gemma Roig’s lab under the Postdoc-NeT-AI program. Slide.
Jun 7, 2023 Finally, I submit my Ph.D. thesis for examination. Phew!
Apr 12, 2023 I have been awarded a DAAD AInet fellowship
Apr 2, 2023 Presenting “Holistic View of Adversarial Machine Learning” at our lab meeting. Slide
Sep 2, 2022 Presenting “Sharpness Aware Minimization: Recent Advances and Applications” at our lab meeting. Slide

latest posts

selected publications

2025

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    Fantastic Targets for Concept Erasure in Diffusion Models and Where to Find Them
    Tuan-Anh Bui, Vu Trang, Vuong Long, and 4 more authors
    International Conference on Learning Representations (ICLR), 2025
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    Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts
    Tuan-Anh Bui, Khanh Doan, Trung Le, and 3 more authors
    ICLR 2025 DeLTa Workshop, 2025

2024

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    Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation
    Tuan-Anh Bui, Vuong Long, Khanh Doan, and 4 more authors
    Advances in Neural Information Processing Systems (NeurIPS), 2024
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    Diversity-Aware Agnostic Ensemble of Sharpness Minimizers
    Tuan-Anh Bui*, Vy Vo*, Tung Pham, and 2 more authors
    Preprint, 2024

2023

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    Optimal transport model distributional robustness
    Van-Anh Nguyen, Trung Le, Tuan-Anh Bui, and 2 more authors
    Advances in Neural Information Processing Systems (NeurIPS), 2023
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    Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
    Tuan-Anh Bui, Trung Le, He Zhao, and 3 more authors
    Transactions on Machine Learning Research (TMLR), 2023

2022

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    A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
    Tuan-Anh Bui, Trung Le, Quan Tran, and 2 more authors
    In International Conference on Learning Representations (ICLR), 2022

2021

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    Understanding and achieving efficient robustness with adversarial supervised contrastive learning
    Tuan-Anh Bui, Trung Le, He Zhao, and 3 more authors
    arXiv preprint arXiv:2101.10027, 2021

2020

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    Improving adversarial robustness by enforcing local and global compactness
    Tuan-Anh Bui, Trung Le, He Zhao, and 4 more authors
    In European Conference on Computer Vision (ECCV), 2020

2019

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    Improving GAN with neighbors embedding and gradient matching
    Ngoc-Trung Tran*, Tuan-Anh Bui*, and Ngai-Man Cheung
    In Proceedings of the AAAI conference on artificial intelligence (AAAI), 2019

2018

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    Dist-gan: An improved gan using distance constraints
    Ngoc-Trung Tran, Tuan-Anh Bui, and Ngai-Man Cheung
    In Proceedings of the European conference on computer vision (ECCV), 2018