Dr Thin Nguyen

📖Biography

My research advances causal artificial intelligence: machine learning that goes beyond correlation to reason about causes, mechanisms, interventions and explanations. The central vision is to build systems that ask not only what is likely to happen, but why it happens, what would change under intervention, and when a learned model can be trusted under distribution shift. To this end I develop methods for causal discovery, causal inference, trustworthy and explainable AI, and causal reasoning in foundation models, with contributions including acyclicity-free and Bayesian causal discovery, conditional-independence testing with neural density models, robust causal direction and noise-model estimation, root-cause explanation, causal domain generalisation, and causal alignment and steering of large language models.

These methods are driven by scientific and public-interest challenges where causal reasoning provides actionable insight, robust decision support and scientific understanding: digital phenotyping and mental health, precision public health and chronic-disease prevention, drug discovery and repurposing, genomics and disease modelling, climate and environmental sustainability, social-science analytics, and scientific machine learning more broadly.

🏛Deakin University current appointment

🎓Degrees

• Doctor of Philosophy (Computer Science), 10/2008–02/2012, Curtin University, Perth, WA, Australia.

🧠Fields of research

🎓Availability for supervision

PhD supervision. I welcome enquiries from prospective PhD students interested in causal AI and its applications — email thin.nguyen@deakin.edu.au.

Area / Faculty

Research and Innovation

Department / School / Institute / Discipline

Applied Artificial Intelligence Initiative