Matteo Bonotto, Ph.D.

I am a ML research engineer with deep expertise in machine learning, scientific computing, and nuclear fusion energy. I have several years of experience as a research engineer in machine learning, computational physics, and magnetic confinement fusion.

At Giotto.ai, I’m doing core machine learning, with a focus on Transformers and LLMs. I’ve contributed to solutions awarded the gold medal in the Kaggle ARC Prize (2024 and 2025). In my current duties, I’m doing distributed training, implementing and optimizing custom internals of transformer models, and helping maintain our proprietary ML infrastructure.

Previously, I worked at Eni, where I developed AI solutions for industrial applications in the energy sector. Among other things, I’ve been working a lot on disruption prediction, in collaboration with MIT PSFC scientists.

And before that, I’ve been a researcher in magnetic confinement fusion, the same field where I got my Ph.D (my thesis). I’ve been working on mathematical modeling for electromagnetics, plasma equilibrium and magnetohydrodynamics (here some papers). I’ve been developing surrogate models of plasma equilibrium using physics-informed neural operators (see PlaNet and this dataset), and I developed a FEM-based tool for plasma equilibrium with a novel formulation for boundary conditions (FRIDA), and many additional things!