Quantitative Researcher, Marie Curie PhD on Machine Learning at CERN, with a background in Theoretical Physics from Oxford

I work on problems where data is noisy, time-dependent, and decisions must be made in real time. My focus is building models and research pipelines that remain reliable outside the lab — in production, under uncertainty.

My background spans machine learning, time-series analysis, and high-performance computing, with experience building systems that operate on large-scale, real-world data.

Areas of Focus

  • Time-series modeling and validation
  • Backtesting and research pipelines
  • High-performance ML systems

These skills apply across domains such as finance, energy, transportation, and engineering, where data is complex and reliability matters.

Who This Is For

I typically advise small teams and startups working on challenging data problems who need rigorous modeling and robust systems, not quick prototypes.

Contact

For inquiries, see my About page.