Hi, I'm Matteo.
I work at the intersection of machine learning research and engineering.
My work spans foundation models, scientific machine learning, and applications ranging from physiological signals to engineering design.
Scientific ML · Generative design
AIXD
Developing neural surrogates for CFD simulations, multi-fidelity learning, and inverse design in engineering applications.
02Foundation models · Biosignals
TinyMyo
Self-supervised learning for EMG signals, with applications in hand-movement decoding and embedded inference.
03NLP · Multilingual learning
DemocraSci
Research on multilingual stance detection in Swiss parliamentary data.
TinyMyo: A Tiny Foundation Model for Flexible EMG Signal Processing at the Edge
M. Fasulo, G. Spacone, T. M. Ingolfsson, Y. Li, L. Benini, A. Cossettini
IEEE Transactions on Biomedical Engineering
AI Wizards at EXIST 2026: Hierarchical Soft-Label Learning for Multimodal Sexism Identification in Memes
M. Fasulo, A. Gravina, L. Tedeschini, L. Babboni
Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2026). Jena, Germany, 21-24 September 2026.
AIWizards at MULTIPRIDE: A Hierarchical Approach to Slur Reclamation Detection
L. Tedeschini, M. Fasulo
Proceedings of the 9th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026). Bari, Italy, February 26th-27th, 2026.
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection
M. Fasulo, L. Babboni, L. Tedeschini
Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2025). Madrid, Spain, 9-12 September 2025.
Research and
engineering.
My background is in artificial intelligence, with experience across foundation models, self-supervised learning, scientific machine learning, and NLP.
During my master's thesis at ETH Zurich, I worked on TinyMyo, a foundation model for EMG signal processing and hand-movement decoding. I now work at the Swiss Data Science Center on machine learning methods for engineering design.
I'm interested in both the research behind machine learning models and the practical challenges of training, evaluating, and deploying them.