Selected work

Projects.

Where the mathematics meets the machine — research projects spanning distribution shift, benchmarking, and optimization-driven modelling.

Nov 2025 — Feb 2026

ADR Summer Project 2025 · Deakin University

Implemented a range of data-splitting techniques designed to mimic real-world distributionally shifted data.
Benchmarked state-of-the-art ML/DL models under those shifted regimes to probe how generalization degrades off-distribution.
Summarised findings and reviewed the related literature in an academic, reproducible manner.

PyTorchScikit-learnBenchmarkingDistribution ShiftLiterature Review

GitHub repo ↗

2025 — 2026

Companion toolkit · Out-of-Distribution Data Fitting

Built an end-to-end, config-driven pipeline that splits tabular datasets, trains regressors, and evaluates them under out-of-distribution train/test regimes.
Engineered geometric and distributional splitters — hyperball, slab, K-means, and covariate-shift — to synthesise controlled distribution shift from any dataset.
Unified eleven classical and neural regressors (Huber, KNN, SVM, tree ensembles, XGBoost, LightGBM, ResNet) behind one interface, with Slurm batch support for HPC runs.

PythonPyTorchScikit-learnXGBoostLightGBMSlurm / HPC

GitHub repo ↗

Jun 2024

PiMA Research Summer Camp 2024

Transformed regression problems into optimization frameworks and clean matrix formulations.
Built linear-regression models with both L1 (linear programming via SciPy linprog) and L2 (closed-form least squares) approaches.
Implemented and evaluated both in Python, comparing error-minimisation strategies on robustness, accuracy, and sensitivity to outliers.

PythonSciPyLinear ProgrammingLeast SquaresRegression

GitHub repo ↗

New work in graph neural networks and brain-inspired models is in progress. Check back, or follow along on GitHub.