Projects per year
Personal profile
Biography
Dinh Phung is a Professor at Monash University, Australia and a Research Director of the Department of Data Science and AI in the Faculty of IT. His research interest includes machine learning, deep learning, optimal transport, Bayesian and graphical models. He has published 250+ papers in these areas and application domains such as natural language processing (NLP), computer vision, digital health, cybersecurity and autism. Between 2017 - 2020, he was an AI Chief Scientist for Trusting Social - an AI Fintech company to provide Digital Identity and eKYC solutions. Currently he splits his time at Monash working as a Senior Principal Research Scientist/Consultant for VinAI/VinFast. He is also known in Vietnamese as Phùng Quốc Định and his personal website is located here.
Prospective PhD students, Postdocs and Tutors: Thank you for your interest and for reaching out to me. While I'm keen on receiving strong applications and your EoI, due to the large volume of such emails, please accept my apology in advance if you do not receive a response from me individually as I might only be able to respond to short-listed or selected EoI. Please see this link for further information regarding PhD scholarship and admission at Monash.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Computer Science, Doctor of Philosophy, Curtin University
Award Date: 17 Jun 2005
Computer Science, Bachelor of Science (Honours), Curtin University
Award Date: 3 Sep 2001
Research area keywords
- Machine Learning
- artificial intelligence
- deep learning
- representation learning
- Bayesian statistics
- graphical models
- generative AI
- optimal transport
- robust and adversarial machine learning
- NLP and computer vision
- digital health
- cybersecurity
- medical AI
- autism
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Exploiting Geometries of Learning for Fast, Adaptive and Robust AI
Phung, D., Tafazzoli Harandi, M., Hartley, R., Le, T. & Koniusz, P.
Australian Research Council (ARC)
8/05/23 → 7/05/26
Project: Research
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ARC LIEF: Towards a Green and Sustainable Energy-efficient Metaverse
Susilo, W., Bennamoun, M., Venkatesh, S., Phung, D., Fidge, C., Phung, S. L., Boussaid, F., Agalgaonkar, A., Chow, Y., Gupta, S. K., Rana, S., Steinfeld, R., Li, Y., Sakzad, A. & Simpson, L.
Australian Research Council (ARC)
1/05/23 → 30/04/24
Project: Research
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Project 44 - Generative architectural design engine
Cruz Gambardella, C., Phung, D., McCormack, J., Cai, J., Murray, S., Weiss, K., Patterson, D., Huang, S. & Gribble, A.
12/04/22 → 11/04/25
Project: Research
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Optimal Transport Theory for Machine Learning with Limited and Less Labels
Asian Office of Aerospace Research And Development (AOARD)
9/09/21 → 8/09/23
Project: Research
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Generating Team Behaviours with Generative Adversarial Networks
Phung, D., Papasimeon, M., Huynh, V., Zhao, E. & Rezatofighi, H.
23/08/21 → 2/05/22
Project: Research
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A unified Wasserstein distributional robustness framework for adversarial training
Bui, T., Le, T., Tran, Q., Zhao, H. & Phung, D., 2022, International on Learning Representation (ICLR) 2022. LeCun, Y. (ed.). USA: OpenReview, 25 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile -
A Vietnamese-English neural machine translation system
Nguyen, T. H., Nguyen, T. D. H., Phung, D., Nguyen, D. T. C., Tran, H. M., Luong, M., Vo, T. D., Bui, H. H., Phung, D. & Nguyen, D. Q., 2022, Interspeech 2022. Ko, H. & H. L. Hansen, J. (eds.). Baixas FRANCE: ISCA, p. 5543-5544 2 p. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH; vol. 2022-September).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile4 Citations (Scopus) -
Bridging global context interactions for high-fidelity image completion
Zheng, C., Cham, T-J., Cai, J. & Phung, D., 2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. Dana, K., Hua, G., Roth, S., Samaras, D. & Singh, R. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 11502-11512 11 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2022-June).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
6 Citations (Scopus) -
Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation
Nguyen, T., Nguyen, V., Le, T., Zhao, H., Tran, Q. H. & Phung, D., 2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. Cussens, J. & Zhang, K. (eds.). London UK: Proceedings of Machine Learning Research (PMLR), Vol. 180. p. 1519-1529 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile -
Domain generalisation of NMT: fusing adapters with leave-one-domain-out training
Vu, T-T., Khadivi, S., Phung, D. & Haffari, G., 2022, ACL 2022 - The 60th Annual Meeting of the Association for Computational Linguistics - Findings of ACL 2022. Muresan, S., Nakov, P. & Villavicencio, A. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. 582-588 7 p. (Proceedings of the Annual Meeting of the Association for Computational Linguistics).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile1 Citation (Scopus)