WORK EXPERIENCE
January 2023 – January 2025
Senior Research Fellow
CERN
Responsible for operations at one of CERN’s experimental areas and perform
Monte Carlo simulations via Fortran and C++-based software. I then perform
thorough data analysis in Python using Root, matplotlib, and Scipy libraries for
future experiments to optimize efficiency and reduce production costs.
Key expertise: Python, C++, Scipy, Optimization
October 2019 – January 2023
Doctoral Research Assistant
CERN
Designed and developed a new model of the beamline for a future experiment. Monte-Carlo simulations and data analysis via Pyhton of results presented to the group and at international conferences. This project set the foundations for real-life implementations of the machine with realistic technology.
Key expertise: Python, C++, Scipy, Geant4
2017 – 2019
Professor Assistant
University of Milano – Bicocca
Teaching how to solve basic exercises to first-year students at the Physics Department on
Mechanical Physics, Thermodynamics, and Basic Gravitational Physics. Provide help and
instructions to second-year students at the Physics department at the Milano-Bicocca
University Laboratory on Optical Physics.
Key expertise: Communication, organization
EDUCATION
Ph.D. Particle Physics
Università degli Studi Milano -Bicocca
Summa cum Laude
M.Sc. Particle Physics
Università degli Studi Milano – Bicocca
110 cum Laude / 110
Bachelor Physics
Università degli Studi Milano – Bicocca
Affiliations
CERTIFICATES & TRAININGS
01
Databases and SQL for Data Science with Python
SQL fundamentals for data science applications. It covers relational databases, writing queries, data filtering, and advanced SQL techniques like JOINs. Learners also integrate SQL with Python for data analysis. Efficiently manage and analyze data using SQL and Python.
IBM
02
Decision-Making and Scenarios
Enhance learners’ abilities to utilize quantitative models for informed business decisions.
Structuring decision-making processes, applying models to real-world business and financial scenarios, and improving problem-solving skills through data analysis. Apply quantitative modeling techniques to complex decision-making situations.
The University of Pennsylvania.
03
Fundamentals of Quantitative Modeling
principles of building and using models for data-driven decision-making. It covers linear models, optimization, and probabilistic models to analyze and predict outcomes. Develop problem-solving skills using quantitative methods. Apply modeling techniques to real-world business and finance scenarios.
The University of Pennsylvania.
04
Modeling Risk and Realities
How to build quantitative models that address uncertainty and risk in decision-making. It covers techniques for modeling both low and high-uncertainty scenarios, using predictive analysis and optimization tools. Create models that help make data-driven decisions while managing risk effectively.
The University of Pennsylvania.
05
Python and Statistics for Financial Analysis
How to use Python and statistical methods to analyze financial data. It covers importing, cleaning, and visualizing stock data and applies statistical techniques to make informed investment decisions. By the end, learners will be able to analyze financial data using Python and statistical inference to support data-driven financial decisions.
The Hong Kong University of Science and Technology
06
Introduction to Spreadsheets and Models
How to use spreadsheets for data organization and analysis. It covers building simple models for mapping data and forecasting trends, as well as performing decision analysis. Be able to use spreadsheets to support data analysis and decision-making.
The University of Pennsylvania
LANGUAGES
English
Fluent C1
Italian
Native
French
Fluent B2
Japan.
Beginner
Hobbies
Rock Climbing Chess Photography Travel