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
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
Distribution of avalanches in disordered systems
Supervisor:
Université Paris Sud (Université Paris-Saclay)
Scholarship by the École Normale Supérieure
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
Ranked 1st among all the participants
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.
Ranked 4th among over 600 candidates.
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)
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)
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)
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)
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)
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)
Comparing dynamics: deep neural networks versus glassy systems
ICML, PMLR 80:314-323 (PMLR)
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)
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
-
Slides: Jamming transition in deep networks. Good performance of overparametrized networks. Double descent in generalization error. Fluctuations and ensemble averaging.
- Slides: Learning curves of kernel methods. Curse of dimensionality? Performance on real data. Teacher-Student kernel regression. Effective dimension and smoothness of real data. RKHS assumption. Dimensionality reduction due to task invariance for kernel regression and classification.
- Other slides