Top of page

Student/Staff Portal
Global Site Navigation

School of Science

Local Section Navigation
You are here: Main Content
  • Dr Viet Huynh

    Dr Viet Huynh is a lecturer in ECU's School of Science

Related Contents

Dr Viet Huynh

Lecturer

Staff Member Details
Email: v.huynh@ecu.edu.au
Campus: Joondalup  
Room: JO18.314  

Viet is a lecturer in Computer Science in the School of Science.

Current Teaching

  • CSI6207 Systems Analysis and Database Design
  • CSI3106 Software Architectures and Design

Background

Viet Huynh is a lecturer specializing in machine learning and AI. Before his tenure at ECU, he served as an associate research fellow at the PRaDA (now A2I2) centre at Deakin University and as a research fellow on the Machine Learning team at Monash University. His Ph.D., obtained from Deakin University under the guidance of Professor Dinh Phung and Professor Svetha Venkatesh, focused on scaling up learning algorithms for probabilistic graphical models. Viet boasts an impressive publication record with over 30 papers presented in top-notch conferences and esteemed journals in machine learning and artificial intelligence (ML/AI), including NeurIPS, ICML, ICLR, and JMLR. He has consistently contributed as a reviewer for major ML/AI conferences such as NeurIPS, ICML, ICLR, and AMML. His research interests span large-scale learning algorithms for probabilistic graphical models, the application of deep generative models for learning with probabilistic graphical models, Bayesian optimization, and causal machine learning. Additionally, he is dedicated to applying machine learning/AI methodologies to directly impact various domains, including healthcare, computational biology, cybersecurity, and manufacturing optimization.

Professional Memberships

  • 2019 - Association for Computing Machinery, ACM (Member)

Research Areas and Interests

  • Methods in Machine Learning and Artificial Intelligence
  • Causal Machine Learning: causal discovery and inference
  • Deep Generative Models
  • Applications of optimal transport theory to understand challenging problems in machine learning and deep learning.
  • Bayesian optimization

Research Student Supervision

Skip to top of page