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.
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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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
Us Department Of Air Force - Asian Office Of Aerospace
9/09/21 → 8/09/23
Project: Research
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Towards a National Data Management Platform and Learning Health System
Teede, H., Kannan, A., Andrew, N., Pearce, C., Johnson, A., Enticott, J., Bonnington, P., Dobson, R., Curcin, V., McKimm, A., Wale, R., Bingham, G., Tong, E., Mason, C. W., MacBean, L., Pearce, C., Ferrigi, J., Andrew, N., Beare, R., Srikanth, V., Phung, D., Du, L., Collyer, T., Shaw, T., Snelling, T. L., Keech, W., Geelhood, G. & Wilson, A.
1/02/21 → 30/06/22
Project: Research
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ARC Research Hub for Digital Enhanced Living
Mouzakis, K., Grundy, J., Venkatesh, S., Maeder, A. J., Hutchinson, A. M., Berk, M., Maddison, R., Kouzani, A. Z., Vasa, R., Calvo, R., Christensen, H. M., Williams, P., Phung, D., Yearwood, J., Gordon, S., Powers, D. M. W., Wickramasinghe, N., Bidargaddi, N., Rana, S., Tran, T., Gupta, S., Luo, W., Abdelrazek, M., Tan, F. T. C., Langberg, H., Kayser, L., Kensing, F., Bodendorf, F., Hansen, J. P., Warren, J. R., Sinha, R., Smeaton, A., Aitken, I., Voukelatos, F., Fiebig, J., Serroni, D., Farquhar, C., Nagarajan, R., Tripodi, B., Biggin, J., Fouyaxis, J., Gerasimou, E., Varley, D., Pitcher, M. & Rudolph, C.
Cancer Council Victoria , Interrelate Limited, Health Metrics Pty Ltd, Uniting Care NSW ACT, Dementia Australia Limited, Uniting Agewell Limited, Black Dog Institute, Monash University – Internal Faculty Contribution
15/03/18 → 14/03/23
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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Improving kernel online learning with a snapshot memory
Le, T., Nguyen, K. & Phung, D., Mar 2022, In: Machine Learning. 111, p. 997–1018 22 p.Research output: Contribution to journal › Article › Research › peer-review
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Explain2Attack: text adversarial attacks via cross-domain interpretability
Hossam, M., Le, T., Zhao, H. & Phung, D., 2021, Proceedings of ICPR 2020, 25th International Conference on Pattern Recognition. Boyer, K., C.Lovell, B., Pelillo, M., Sebe, N., Vidal, R. & Yu, J. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 8922-8928 7 p. (Proceedings - International Conference on Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Generalised unsupervised domain adaptation of neural machine translation with cross-lingual data selection
Vu, T., He, X., Phung, D. & Haffari, R., 2021, 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Huang, X., Specia, L. & Wen-tau Yin, S. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. 3335–3346 12 p. 268Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile1 Citation (Scopus) -
The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology
Knott, R., Johnson, B. P., Tiego, J., Mellahn, O., Finlay, A., Kallady, K., Kouspos, M., Mohanakumar Sindhu, V. P., Hawi, Z., Arnatkeviciute, A., Chau, T., Maron, D., Mercieca, E. C., Furley, K., Harris, K., Williams, K., Ure, A., Fornito, A., Gray, K., Coghill, D. & 7 others, , 5 Aug 2021, In: Molecular Autism. 12, 1, 24 p., 55.Research output: Contribution to journal › Article › Other › peer-review
Open Access1 Citation (Scopus) -
A capsule network-based model for learning node embeddings
Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q. & Phung, D., 2020, Proceedings of the 29th ACM International Conference on Information & Knowledge Management. Hauff, C., Curry, E. & Cudre Mauroux, P. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 3313-3316 4 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review