I build neural network surrogates for granular material simulation and physics-informed ML systems and I'm increasingly focused on applied AI/agentic engineering. Currently exploring industry roles in ML/AI engineering, robotics, and computational modelling across Australia.
- 👨🎓 PhD in Engineering — Federation University Australia (with CSIRO, fully RTP-funded) Thesis: "Accelerated Surrogate Modelling of Granular Materials using Artificial Neural Networks"
- 👨🎓 Master of Engineering, Mechatronics (GPA: 6.2/7.0) — Australian National University
- 👨🎓 Bachelor of Engineering, Mechatronics — Ho Chi Minh City University of Technology, Vietnam
- Deep Learning & Neural Networks
- Discrete Element Method (DEM) & Surrogate Modelling
- Physics-Informed Neural Networks
- Computational Modelling & Simulation
- Mechatronics Design & Embedded Systems
| Project | Description |
|---|---|
| simple_notebooklm | A document-grounded RAG learning assistant inspired by NotebookLM — upload PDFs, get grounded Q&A, summaries, quizzes, and flashcards with source citations. |
| urbancool_melbourne (demo) | ML dashboard predicting urban heat vulnerability across Greater Melbourne from a Random Forest model (R² = 0.57): FastAPI backend, interactive SHAP explanations, and a live green-infrastructure what-if simulator. |
| eco_risk_au | Geospatial + LLM ecosystem climate-risk platform for Victoria, Australia: PostGIS ETL pipeline computing a composite climate risk score per vegetation patch, a LangGraph + Gemini RAG pipeline extracting structured risk data from real IUCN Red List of Ecosystems documents, and a React + Leaflet dashboard. |
AI / ML
Systems / Infra
Open to ML/AI engineering, robotics, and computational modelling roles in Australia — feel free to reach out!