Dinh Phung


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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.

Prof Dinh Phung is currently the Chief Examiner for the new FIT3181 Deep Learning unit, which will be offered in 2019. His personal website is at: dinhphung.ml and he's also known in Vietnamese as Phùng Quốc Định.


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

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

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

Stay Well: Analysing Lifestyle Data from Smart Monitoring Devices (ARC DP)

Phung, D., Venkatesh, S. & Kumar, M.


Project: Research

Research Output 2013 2019

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

Robust anomaly detection in videos using multilevel representations

Vu, H., Nguyen, T. D., Le, T., Luo, W. & Phung, D., 2019, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). Van Hentenryck, P. & Zhou, Z-H. (eds.). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 5216-5223 8 p. 2579. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 33, no. 1).

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

Open Access

Sqn2Vec: learning sequence representation via sequential patterns with a gap constraint

Nguyen, D., Luo, W., Nguyen, T. D., Venkatesh, S. & Phung, D., 2019, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018 Dublin, Ireland, September 10–14, 2018 Proceedings, Part II. Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N. & Ifrim, G. (eds.). Cham Switzerland: Springer, p. 569-584 16 p. (Lecture Notes in Computer Science ; vol. 11052 ).

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

Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices

Nguyen, T., Larsen, M., O'Dea, B., Nguyen, H., Nguyen, D. T., Yearwood, J., Phung, D., Venkatesh, S. & Christensen, H., 2019, (Accepted/In press) In : Future Generation Computer Systems. 9 p.

Research output: Contribution to journalArticleResearchpeer-review

Academia versus social media: a psycho-linguistic analysis

Nguyen, T., Venkatesh, S. & Phung, D., Mar 2018, In : Journal of Computational Science. 25, p. 228-237 10 p.

Research output: Contribution to journalArticleResearchpeer-review