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Personal profile


Dinh Phung is a Professor at Monash University, Australia and a Research Director of the Department of Data Science and AI in the Faculty of IT. His research interest includes machine learning, deep learning, optimal transport, Bayesian and graphical models. He has published 250+ papers in these areas and application domains such as natural language processing (NLP), computer vision, digital health, cybersecurity and autism. Between 2017 - 2020, he was an AI Chief Scientist for Trusting Social - an AI Fintech company to provide Digital Identity and eKYC solutions. Currently he splits his time at Monash working as a Senior Principal Research Scientist/Consultant for VinAI/VinFast. He is also known in Vietnamese as Phùng Quốc Định and his personal website is located here.

Prospective PhD students, Postdocs and Tutors: Thank you for your interest and for reaching out to me. While I'm keen on receiving strong applications and your EoI, due to the large volume of such emails, please accept my apology in advance if you do not receive a response from me individually as I might only be able to respond to short-listed or selected EoI. Please see this link for further information regarding PhD scholarship and admission at Monash.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 13 - Climate Action
  • SDG 16 - Peace, Justice and Strong Institutions

Education/Academic qualification

Computer Science, Doctor of Philosophy, Curtin University

Award Date: 17 Jun 2005

Computer Science, Bachelor of Science (Honours), Curtin University

Award Date: 3 Sep 2001

Research area keywords

  • Machine Learning
  • artificial intelligence
  • deep learning
  • representation learning
  • Bayesian statistics
  • graphical models
  • generative AI
  • optimal transport
  • robust and adversarial machine learning
  • NLP and computer vision
  • digital health
  • cybersecurity
  • medical AI
  • autism


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