Francesco Paissan

Francesco Paissan,

Researcher, Fondazione Bruno Kessler

Visiting Researcher, Mila

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

Talks, seminars, lectures

tinyML EMEA Innovation Forum - "tinyCLAP: towards embedded foundational models" [slides]
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"

Timeline

Our Listenable Maps for Zero-Shot Audio Classifiers was accepted at NeurIPS 2024.
Our Listenable Maps for Audio Classifiers was accepted at ICML 2024 as Oral!
Our tinyCLAP and Audio Editing with Non-rigid Text Prompt papers were accepted to Interspeech 2024.
Many things, I decided to start doing this too late. :)

Contact

email: francescopaissan at gmail dot com

twitter: @fpaissan_