Stefano Carrazza

Associate Professor

Biography

I have been a graduate student at École Normale Supérieure de Lyon (ENS), a PhD student at the University of Milan, and a CERN fellow.

My research is mostly focussed on computational models and methods applied to science. I had the opportunity to perform scientific research in theoretical and experimental physics at different institutions. I had experience in quantum optics at the Institut Lumière Matière in Lyon, cavity quantum electrodynamics at the Laboratoire Kastler-Brossel (ENS) in Paris with the Nobel Price Serge Haroche, data analysis and software development for heavy-ion physics in ALICE at CERN, theoretical physics at CERN and UNIMI, and quantum computing scientific collaborations with CQT, UB, CERN.

Interests

Computational Physics
Parton Distribution Functions
Monte Carlo Simulation
Artificial Intelligence
Quantum Computing
AI & Medical Physics
Big data for Economics

Education

PhD in Physics, Astrophysics and Applied Physics

University of Milan (Italy)
MSc in Sciences of Matter and Physics

École Normale Supérieure (France)
BSc in Physics

École Normale Supérieure (France)

Experience

Research

Associate Professor

University of Milan

Oct 2021 – Present Milan, Italy

Senior Researcher

University of Milan

Oct 2018 – Sep 2021 Milan, Italy

Senior Research Fellow

CERN, Theoretical Physics Department

Oct 2015 – Sep 2018 Geneva, Switzerland

Postdoctoral Fellow

University of Milan

Nov 2014 – Sep 2015 Milano, Italy

Teaching

Ongoing lectures

Deep Learning - Master Degree

University of Milan

Mar 2022 – Present Milan, Italy

Computer Science - Bachelor Degree

University of Milan

Oct 2018 – Present Milan, Italy

Tecniche di calcolo e sistemi operativi - SSFM

University of Milan

Oct 2019 – Present Milan, Italy

Introduction to Machine Learning - PhD in Physics

University of Milan

Oct 2018 – Present Milan, Italy

An introduction to machine learning techniques including model representation, parameter learning, non-linear models, hyperparameter tune, and an overview of modern deep learning strategies. The seminars will cover the theoretical and mathematical aspects of machine learning followed by practical examples of code implementation using public frameworks.