Selected work / 2020—2026

Projects

A selection of projects at the intersection of data science, quantitative research, and real-world operations.

01Mining technology

Underground Mining Scheduler

From operational complexity to a coordinated shift.

A real-time scheduling dashboard and constraint-based engine for underground mine development. It coordinates work stages, equipment availability, blasting windows, delays, and dependencies across headings.

Minute-by-minute stage progression and shift coordination
Equipment assignment with specialty and travel-time constraints
Schedule recalculation when operational delays or work requirements change

Constraint optimisationTypeScriptSchedulingProduct design

View case study Explore live prototype

02Quantitative finance

Modelling Systemic Risk with ΔCoVaR

Comparing linear and neural-network quantile regression.

A quantitative-finance research project comparing linear quantile regression and neural-network quantile regression for estimating ΔCoVaR, a measure of an institution’s contribution to systemic financial risk.

Compared linear and neural-network approaches to ΔCoVaR estimation
Worked with weekly data spanning 2003–2023 across US and euro-area banks
Examined trade-offs between historical fit and forward-looking prediction

PythonNeural networksQuantile regressionRisk modelling

View case study Open research notebook

03Portfolio optimisation

Benchmark-Neutral Allocations with Factor Tilts

Balancing investor preferences and benchmark tracking.

A portfolio-optimisation project that introduces targeted factor or sector tilts while keeping allocations close to a market-capitalisation-weighted benchmark and controlling active risk.

Optimised the balance between tracking error and targeted factor exposures
Applied factor extraction and multicollinearity checks to market data
Explored signals across technology, healthcare, consumer, finance, and energy

PythonPortfolio optimisationFactor modelsMarket data

View case study View project on GitHub