Francesco Paissan
Research Scientist Intern at Mitsubishi Electric Research Laboratories
Rovereto, Trentino – Alto Adige/Südtirol, Italy
Summary
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Rockstar🎓
Top SchoolFrancesco Paissan is a research-focused software engineer with six years of experience at the intersection of machine learning, signal processing, and embedded systems. He has held research roles at FBK, INFN, Mila, and currently at Mitsubishi Electric Research Laboratories, bringing hands-on expertise in experimental design and evaluation of ML models. His contributions to the popular PyTorch-based SpeechBrain toolkit emphasize analysis tooling—aggregating and visualizing results and integrating EEG training-paradigm metrics—revealing a strengths in reproducible experimentation and metrics-driven research. Based in Rovereto, Italy, he combines academic rigor with practical development experience from robotics and embedded software roles earlier in his career. Colleagues describe him as someone who bridges low-level systems work and high-level model evaluation, making complex pipelines more interpretable. He often focuses on the less-visible but crucial infrastructure that turns research experiments into reliable, comparable results.
6 years of coding experience
2 years of employment as a software developer
High School Diploma, Applied sciences, High School Diploma, Applied sciences at Liceo Antonio RosminiBachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Università di Trento
Stackoverflow
29reputation
2kreached
1answer
1question
Github Skills (14)
pytorch10
evaluation10
metric10
data-analysis10
python9
machine-learning9
deep-learning8
speech-recognition8
audio-processing8
speech-processing8
recipe7
opencv6
ffmpeg6
asr5
Programming languages (4)
Github contributions (5)
speechbrain/speechbrain
Oct 2022 - Oct 2022
A PyTorch-based Speech Toolkit
Role in this project:
Data Scientist
Data ScientistContributions:7 reviews, 1 commit, 8 PRs in 1 day
Contributions summary:Francesco's contributions primarily focused on developing scripts for aggregating and visualizing results, indicating involvement in the analysis and evaluation of machine learning models. They are refining a script to aggregate results based on different EEG training paradigms, and incorporating metrics from the training process. These changes suggest an active role in the experimentation and evaluation of machine learning models within the speech toolkit.
voice-recognitionasrspeech-recognitionspeech-separationspeaker-verification
micromind-toolkit/micromind
Jul 2021 - Feb 2023
A toolkit for tinyML research and deployment
Contributions:2 releases, 26 reviews, 97 commits in 1 year 6 months
analyticspythonevaluatepippip-package
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