20112019

Research output per year

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Personal profile

Biography

Viet Huynh is currently a postdoctoral researcher at the Machine Learning team at Monash University. Before going to Monash, he was a postdoctoral researcher in the PRaDA (Pattern Recognition and Data Analytics) center at Deakin University.

He was a Ph.D. student in PRaDA center at Deakin University from 2013 to early 2017. He worked under the supervision of Professor Dinh Phung  and Professor Svetha Venkatesh. His Ph.D. work focused on resorting big data to actionable information involves dealing with four dimensions of challenges in big data (called four V’s): volume, variety, velocity, veracity. In this research project, he sought for novel Bayesian nonparametric models and scalable learning algorithms which can deal with these challenges of the big data era.

He also received his B.Eng. and M.Eng. degrees in computer science in 2005 and 2009 respectively, all of which were completed at University of Technology, Vietnam.

Research interests

His current specific research topics that he is interested in and currently working on:

  • Developing large-scale learning algorithms for probabilistic graphical models with complex and large-scale data
  • Applying optimal transport theory to understand challenging problems in machine learning and deep learning.
  • Applying deep generative models for learning with probabilistic graphical models.

Education/Academic qualification

Computer Science, PhD, Deakin University

24 Aug 20132 Feb 2017

Research area keywords

  • Machine Learning
  • Artificial Intelligence
  • Bayesian non-parametrics
  • Probabilistic Graphical Models (PGM)
  • Stochastic Processes
  • Large-scale Graphical Models
  • Bayesian Inference
  • Pervasive Computing
  • Deep Generative Models
  • Image analysis

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Research Output

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

Computing crowd consensus with partial agreement

Hung, N. Q. V., Huynh, V., Tam, N. T., Weidlich, M., Yin, H. & Zhou, X., Jan 2018, In : IEEE Transactions on Knowledge and Data Engineering. 30, 1, p. 1-14 14 p.

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Forward-backward smoothing for hidden Markov models of point pattern data

Dam, N., Phung, D., Vo, B. N. & Huynh, V., 2017, Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017: Tokyo, Japan 19-21 October 2017 . Washio, T., Gama, J., Li, Y., Parekh, R., Liu, H., Bifet, A. & D. De Veaux, R. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 252-261 10 p.

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

Multilevel clustering via wasserstein means

Ho, N., Nguyen, X., Yurochkin, M., Bui, H. H., Huynh, V. & Phung, D., 2017, 34th International Conference on Machine Learning (ICML 2017): Sydney, Australia 6 - 11 August 2017 . Precup, D. & Whye Teh, Y. (eds.). Stroudsburg PA USA: International Machine Learning Society (IMLS), Vol. 3. p. 2363-2378 15 p.

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

Streaming clustering with Bayesian nonparametric models

Huynh, V. & Phung, D., 4 Oct 2017, In : Neurocomputing. 258, p. 52-62 11 p.

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)