About

Quantitative Researcher with a background in Theoretical Physics (Oxford) and a PhD in Machine Learning (CERN), working on time-series modeling, statistical signal research, and real-time decision systems. Experienced in building, validating, and deploying data-driven models in production environments, with strong expertise in high-frequency data, statistical modeling, and high-performance computing.

Occasionally, I advise small teams and startups facing real-world time-series and ML challenges, from high-performance backtesting to robust model deployment.

Contact

You can contact me on LinkedIn or using the contact form.

Education

• PhD in High-Energy Physics
  CERN/Sorbonne University, Switzerland/France
  Oct 2022 – Oct 2025
  • Thesis: “Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures”
• MSc in Applied Mechanics
  National Technical University of Athens (NTUA), Greece
  Oct 2020 - Jul 2022
  • Thesis: “Quantum Chaos in Many-Body Systems Without a Classical Analogue”
• MMathPhys in Mathematical and Theoretical Physics
  University of Oxford, United Kingdom
  Oct 2015 – Jul 2019
  • First Class and Distinction
  • Thesis: “On the Yang–Mills Existence and Mass Gap Problem”