Francesco Paissan

Francesco Paissan, Research fellow, Fondazione Bruno Kessler Research intern / Collaborator, Mila Student, University of Trento

I am a student at the University of Trento, currently enrolled in the computer engineering undergrad program. My research focuses on designing efficient neural networks and training paradigms that enable fast, low-footprint inference and on-device network training. Following my research internship at Mila, I am collaborating with Prof. Subakan and Prof. Ravanelli on interpretability techniques for neural networks, specifically for speech and audio. I am a contributor of the SpeechBrain project, and one of the core contributors of the SpeechBrain-MOABB benchmark. I co-organized the ICASSP 2024 XAI-SA workshop.

You can see my google scholar here.

Research Interests

• Resource-efficient machine/deep learning

• Interpretable Machine Learning, Explainability, Posthoc Interpretations

• Audio generation/editing using neural models

• Machine Learning, Statistical Learning

Representative papers

• Listenable Maps for Audio Classifiers

• PhiNets: a scalable backbone for low-power AI at the edge

• Audio editing with non-rigid text prompts

Talks, seminars, lectures

• UniPD Elements of Deep Learning doctoral course - "An introduction to tinyML" [slides]

• tinyML Foundation webcast series - "tinyML: Designing Ecient Neural Archictures and Scaling Strategies for Edge Computing" [slides]

• UniPD DEI Seminar - "tinyML: neural networks design principles, scaling strategies and beyond" [slides]

• IEEE World Forum on IoT - "Hands‐on tinyML for IoT, bringing intelligence to the edge" [slides]

• INFN/UniRoma3 Seminar - "Emerging Opportunities of Machine Learning in Physics"

Contact

email: francescopaissan at gmail dot com

twitter: @fpaissan_