Dinh Phung

Professor

20122022
If you made any changes in Pure these will be visible here soon.

Personal profile

Biography

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

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.

15/04/1914/04/20

Project: Research

Deep Learning for Cyber (Data61 CRP 38)

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

15/06/1830/06/20

Project: Research

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

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

7/06/1831/12/19

Project: Research

Research Output 2012 2019

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

Maximal divergence sequential auto-encoder for binary software vulnerability detection*

Le, T., Nguyen, T., Le, T., Phung, D., Montague, P., De Vel, O. & Qu, L., 2019, International Conference on Learning Representations 2019. Rush, A. (ed.). La Jolla CA USA: International Conference on Learning Representations (ICLR), 15 p.

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

Open Access
File

Probabilistic multilevel clustering via composite transportation distance

Ho, N., Huynh, V., Phung, D. & Jordan, M. I., 2019, Proceedings of Machine Learning Research : Volume 89: The 22nd International Conference on Artificial Intelligence and Statistics, 16-18 April 2019. Chaudhuri, K. & Sugiyama, M. (eds.). Sheffield UK: Proceedings of Machine Learning Research (PMLR), p. 3149-3157 9 p. (Proceedings of Machine Learning Research; vol. 89).

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

Open Access
File

Robust anomaly detection in videos using multilevel representations

Vu, H., Nguyen, T. D., Le, T., Luo, W. & Phung, D., 2019, Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence. 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
File