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Description of the modular machine-learning anomaly-detection framework developed for ITER’s Tokamak Systems Monitor.

Proof of concept for detecting anomalous gyrotron pulses in ITER’s Tokamak Systems Monitor using dimensionality reduction and clustering.

A machine-learning-based calibration strategy for the ITER Tokamak Systems Monitor, using Bayesian optimization and Gaussian-process regression to efficiently update finite-element models.

Participation to the 31st IEEE Symposium on Fusion Engineering (SOFE 2025) in Boston, presenting a scientific poster on machine learning-based anomaly detection for the ITER Tokamak Systems Monitor (TSM), using gyrotron pulse data from the European industrial prototype for ITER.

Participation to the 33rd Symposium on Fusion Technology (SOFT 2024) in Dublin, presenting a scientific poster on a machine learning-based calibration strategy for the ITER Tokamak Systems Monitor (TSM) software suite.

This study focuses on the use of dimensionality reduction techniques to assess the structural heterogeneity of glassy binary mixtures. This work is in collaboration with Daniele Coslovich and Robert L. Jack.

partycls is a Python package for the analysis of systems of interacting particles using unsupervised machine learning methods. A new major release (2.0.0) was just published.

hamoco v1.0.1 is now available on PyPI for Linux and Windows. Take control of your mouse by using hand gestures captured in real time by your webcam.

Synth Road is an arcade mobile game with synthwave vibes created using the Unity game engine. Any money generated by the optional advertisement will be donated to humanitarian NGOs.

partycls is a Python package for the analysis of systems of interacting particles using unsupervised machine learning methods. A paper on the code, written in collaboration with Daniele Coslovich, was just published in The Journal of Open Source Software.