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

Quantitative research and machine learning expertise, focused on rigorous modeling, validation, and real-world deployment of data-driven systems on high-performance architectures.

Recent Work

  • High-frequency time series modeling
  • Low-latency inference on GPUs and FPGAs
  • Anomaly detection in high-dimensional streaming data
  • Systematic strategy design and backtesting
  • Stochastic processes and statistical inference

Selected Projects

Systematic Bitcoin Strategy Backtester

A trend-following backtesting framework written in Python with Pandas and NumPy. Computes performance metrics including:

  • PnL
  • Sharpe Ratio
  • Max Drawdown

Deployed a Freqtrade crypto trading bot on AWS with Docker, running 24/7 on a live Kraken portfolio.

Resources and Code: Project, Blog

Anomaly Detection on High-Frequency Data

Used a Spatio-Temporal Generative Adversarial Network (GAN) to detect rare events in real-time streaming data from 125+ sources. This method combines:

  • Graph Neural Networks (GNNs) to capture spatial features
  • LSTMs to capture temporal features
  • GANs to efficiently generate realistic time series matching the training data

The methodology applies naturally to:

  • Market anomaly detection
  • Regime shift identification
  • Volatility clustering

Resources and Code: Project

GNN-Based Real-Time Inference

Built a Graph Neural Network pipeline for fast particle tracking at CERN, achieving:

  • 100× GPU inference speedup (vs baseline)
  • 3× energy efficiency (on FPGA)

These optimizations are directly transferable to:

  • Latency-sensitive trading systems
  • Order book modeling
  • Market microstructure feature extraction

Resources and Code: Project

Blog Posts

You can find my blog posts related to finance and investing usings the tags: quant and financial-markets.

Publications & Talks

You can find a full list of my academic work here, including several talks on fast ML for inference and detection—topics that align closely with low-latency trading and online portfolio monitoring.

Technical Stack

Programming: Python, C++, Julia, SQL

Finance: Backtesting, alpha modeling, systematic strategies

Statistics: Regression, time series analysis, stochastic processes

ML: PyTorch, TensorFlow, Scikit-Learn, GNNs, Anomaly Detection

HPC: CUDA, GPU/FPGA acceleration, model optimization