./Stefano aboutrésuméeducationpublicationsslides
My name is Stefano Spigler. Accomplished software developer with a background in physics and deep learning, specializing in AI model development for various industries. Known for a curious problem-solving approach and exceptional soft skills, including strong adaptability and effective communication. Demonstrated expertise in data analysis, computer programming, and machine learning. Proven leadership in managing cross-functional teams and consistently delivering projects ahead of schedule. Committed to fostering team collaboration and valuing individual contributions.


Industry jobs & academic positions

Download the full CV (pdf)
2023- Software Engineer, Machine Learning, Bard
Google, Zurich CH
2022-2023 Senior Data Scientist
Unit8, Zurich CH
2020-2022 Senior AI Model Develop
UBS, Zurich CH
2017-2020 Postdoc researcher
Physics of Complex Systems Laboratory
École Polytechnique Fédérale de Lausanne, Lausanne CH

In collaboration with Prof. Matthieu Wyart

Education

2014-2017 PhD in Physics
Distribution of avalanches in disordered systems
Supervisor: Silvio Franz
Laboratoire de Physique Théorique et Modèles Statistiques
Université Paris Sud (Université Paris-Saclay)

Scholarship by the École Normale Supérieure
You can read here my Ph.D. thesis
2012-2014 Master in Physics of Complex Systems (link)
Politecnico di Torino, International School for Advanced Studies, International Centre for Theoretical Physics, Université Pierre et Marie Curie, Université Paris Diderot, Université Paris Sud, École Normale Supérieure de Cachan
Ranked 1st among all the participants
You can read here my M.Sc. thesis done at the Laboratoire de Physique Théorique et Modèles Statistiques under the supervision of Silvio Franz
2013 Scholarship awarded by Université Paris Sud
2009-2012 Full-merit scholarship awarded by Scuola Galileiana di Studi Superiori (link)
Ranked 4th among over 600 candidates.
2009-2012 Laurea in Physics (BSc)
Università degli Studi di Padova, Padova IT

Skills

Generic programming Python
C++
(X)HTML, CSS, PHP, JavaScript, SQL
Backend, Frontend, Data Engineering Azure Cloud
RESTful APIs (FastAPI)
React
Node.js
Palantir Foundry
Versioning, Editing, Operating Systems LaTeX
Office suite
Unix, Windows
Git (GitHub, GitLab, BitBucket)
ML/AI & Data Science Pytorch, Hugging Face transformers
Pandas, Numpy, Scipy
SciKit-learn
Spacy 3, neuralcoref, CoreNLP, NLTK
Unit8 Darts
OpenAI GPT
Languages Italian (native)
English (full command)
French (fluent)
German (B2)
Spanish (beginner)

Publications

2020 M. Geiger, S. Spigler, A. Jacot, M. Wyart
Disentangling feature and lazy training in deep neural networks
submitted to conference (arXiv preprint)
2019 S. Spigler, M. Geiger, M. Wyart
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm
submitted to conference (arXiv preprint)
2019 M. Geiger, A. Jacot, S. Spigler, F. Gabriel, L. Sagun, S. d'Ascoli, G. Biroli, C. Hongler, M. Wyart
Scaling description of generalization with number of parameters in deep learning
to be submitted (arXiv preprint)
2018 S. Spigler, M. Geiger, S. d'Ascoli, L. Sagun, M. Baity-Jesi, G. Biroli, M. Wyart
A jamming transition from under- to over-parametrization affects loss landscape and generalization
NeurIPS 2018 workshop "Integration of Deep Learning Theories" (arXiv preprint)
Journal of Physics A: Mathematical and Theoretical 52 (47), 474001 (JP A)
2018 M. Geiger, S. Spigler, S. d'Ascoli, L. Sagun, M. Baity-Jesi, G. Biroli, M. Wyart
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Phys. Rev. E 100(1), 012115 (PR E)
2018 M. Baity-Jesi, L. Sagun, M. Geiger, S. Spigler, G.B. Arous, C. Cammarota, Y. LeCun, M. Wyart, G. Biroli
Comparing dynamics: deep neural networks versus glassy systems
ICML, PMLR 80:314-323 (PMLR)
2017 S. Franz, S. Spigler
Mean-field avalanches in jammed spheres
Phys. Rev. E 95(2), 022139 (PR E)
2016 S. Franz, G. Gradenigo, S. Spigler
Random-diluted triangular plaquette model: Study of phase transitions in a kinetically constrained model
Phys. Rev. E 93(3), 032601 (PR E)

Main talks and presentations

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