I'm a machine learning researcher at the University of Sydney's DUAL Lab and an Advanced Computing (Honours) student majoring in Computational Data Science and Finance. I build learning systems for continuous dynamics, quantitative forecasting, and local AI orchestration—where mathematical structure and real-world reliability both matter.
| 🔬 Operator learning | 📈 Quantitative ML | 🤖 Agentic systems |
|---|---|---|
| Continuous-time models, flow matching, neural operators, and stable PDE rollouts. | Market microstructure, volatility forecasting, backtesting, and risk-aware evaluation. | Local multi-model agents, MCP servers, async orchestration, and modular AI workflows. |
- Designing operator-latent flow-matching methods for unseen-time forecasting and cross-resolution PDE modeling; first-author work submitted to NeurIPS 2026.
- Finishing a Bachelor of Advanced Computing (Honours) at the University of Sydney, graduating December 2026.
- Turning research ideas into reproducible systems across Setonix, Gadi, and local GPU environments.
🤖 AI AgentA local, multi-model AI framework built around Ollama, Phi-4, and MCP-style orchestration, with task decomposition and asynchronous sub-model coordination. Python · Ollama · MCP · Async systems |
A transformer forecasting pipeline over high-frequency order-book and alternative data, paired with rigorous backtesting and risk modeling. Deep learning · Market microstructure · Backtesting |
|
A scalable, reproducible time-series pipeline using Parquet, Dask, XGBoost, and SHAP for high-dimensional forecasting and interpretation. Python · Dask · XGBoost · SHAP |
Numerical solvers for one-dimensional PDEs, progressing from finite-difference stability checks toward adaptive moving meshes. Scientific ML · Numerical methods · Python |
- Operator-Latent Flow Matching — a continuous-time operator-learning method combining conditional flow matching with stabilized integrating-factor dynamics for accurate, stable, cross-resolution rollouts.
- Quantum Computing in Finance — an SSRN working paper mapping near-term quantum opportunities, risks, and a readiness roadmap for financial institutions; recognized as a top-downloaded paper across multiple SSRN eJournals in October 2025.
PyTorch ·
TensorFlow ·
scikit-learn ·
SciPy ·
RAG ·
LangChain ·
HPC
I founded the Quantum Computing Society, have competed in chess and football, and completed the Everest Base Camp trek. I enjoy ambitious problems, careful experiments, and conversations that cross disciplinary boundaries.
Always happy to talk about operator learning, quantitative research, or thoughtfully engineered AI.
Let's connect →