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.

Research Interest
  • Theoretical Foundations of Machine Learning and AI
  • Optimal Transport and Wasserstein space methods in AI
  • Generative AI and LLMs
  • Robust/Adversarial Machine Learning and Trustworthy AI
  • Revisiting Graphical Models and Causality in the Deep Learning era

Earlier research interests

  • Nonparametric Machine Learning: Bayesian Nonparametrics, Random Finite Sets and Point Process for ML
  • Graphical Models, Probabilistic Inference, Representation Learning, Latent Variable Models
  • Optimisation, Online learning, Learning from Non-stationary Distributions
  • Applications: Medical AI and Digital Health, NLP, Cybersecurity and Digital Identity, Computer vision, AI-enabled Autism Research


  • Doctor of Philosophy (Computer Science, 2005)
    Thesis: Probabilistic and Film Grammar Based Methods for Video Content Understanding
    Curtin University of Technology, Australia.
  • Bachelor of Computer Science (First Class Honours, 2001)
    Thesis: An Investigation into Audio for Content Annotation
    Curtin University of Technology, Australia.
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