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Personal profile


Dinh Phung is a Professor of Machine Learning and Data Science in the Faculty of Information Technology, Monash University, Australia. He is a leading researcher at the forefront of theoretical and applied machine learning with a current focus on generative deep learning, Bayesian nonparametrics and graphical models, optimal transport and point process theory for machine learning. He publishes regularly in the areas of machine learning, AI and data science. He is also a technical consultant as Director of AI Research for Trusting Social - an AI Fintech company whose aim is to advance data science and AI to provide financial access for all.

Research area keywords

  • Machine Learning
  • artificial intelligence
  • deep learning
  • representation learning
  • Bayesian statistics
  • graphical models
  • learning from non-stationary distributions
  • deep generative models
  • data science
  • autism

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Projects 2018 2022

Towards Robust Learning Systems via Amortized Optimization and Domain Adaptation

Le, T., Phung, D., Xiang, Y., Zhang, J., Erfani, S., Rubinstein, B., Leckie, C., Nock, R. & Knight, K.


Project: Research

Deep Learning for Cyber (Data61 CRP 38)

Phung, D., Le, T., Xiang, Y., Zhang, J., Wen, S., Murray, T. & Nock, R.


Project: Research

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.

Interrelate Limited, Health Metrics Pty Ltd, Uniting Care NSW ACT, Neoproducts Pty Ltd, Dementia Australia Limited, Uniting Agewell Limited, Black Dog Institute, Monash University – Internal Faculty Contribution


Project: Research

Research Output 2012 2020

Pair-based uncertainty and diversity promoting early active learning for Person Re-identification

Liu, W., Chang, X., Chen, L., Phung, D., Zhang, X., Yang, Y. & Hauptmann, A. G., Jan 2020, In : ACM Transactions on Intelligent Systems and Technology. 11, 2, 15 p.

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

An effective spatial-temporal attention based neural network for traffic flow prediction

Do, L. N. N., Vu, H. L., Vo, B. Q., Liu, Z. & Phung, D., 1 Nov 2019, In : Transportation Research Part C: Emerging Technologies. 108, p. 12-28 17 p.

Research output: Contribution to journalArticleResearchpeer-review

Deep domain adaptation for vulnerable code function identification

Nguyen, V., Le, T., Le, T., Nguyen, K., Devel, O., Montague, P., Qu, L. & Phung, DI., 2019, International Joint Conference on Neural Networks (IJCNN) 2019. Angelov, P. & Roveri, M. (eds.). IEEE, Institute of Electrical and Electronics Engineers, 8 p. 8851923

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

GoGP: scalable geometric-based Gaussian process for online regression

Le, T., Nguyen, K., Nguyen, V., Nguyen, T. D. & Phung, D., 20 Jul 2019, In : Knowledge and Information Systems. p. 197-226 30 p.

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Learning Generative Adversarial Networks from multiple data sources

Le, T., Hoang, Q., Vu, H., Nguyen, T. D., Bui, H. & Phung, D., 2019, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Kraus, S. (ed.). California USA: International Joint Conferences on Artificial Intelligence, p. 2823-2829 7 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Open Access