Accepting PhD Students

PhD projects

<a href="" onclick="target='_blank';"></a>


Research activity per year

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

Personal profile


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

Award Date: 4 Oct 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


Recent external collaboration on country/territory level. Dive into details by clicking on the dots or