pwd /Users/Fabio/Curriculum vitae
Fabio Cufino
Physics Student
Info
As a highly motivated and enthusiastic undergraduate physics student with a strong focus on data analysis and computer science, I am currently seeking to expand my technical expertise and gain valuable hands-on experience in experimental research, in order to not only enhance my own understanding of complex scientific phenomena, but also to contribute to the development of new and innovative theories and technologies that can have a meaningful impact on the world.
Education
– 2019
High school diploma – Liceo scientifico Pier Paolo Pasolini
Final mark: 100/100
Outstanding achievement:
Thanks to my dedication, I was given the opportunity to visit CERN.
– 2023
Bachelor’s degree in Physics – University of Padua
Thesis: “Inverse Beta Decay events selection in JUNO using Machine Learning algorithms”
2023 –
Master’s degree in Physics – Advanced Methods in Particle Physics
The IMAPP is an international degree programme leading to a joint qualification, offered by the three partner universities: Clermont Auvergne University (France), TU Dortmund University (Germany) and the University of Bologna (Italy)
2023/2024
University Diploma in Data Science.
Completed at Université Clermont Auvergne, Ecole Universitaire de Physique et d’Ingénierie.
Graduated with honors (Très Bien).
Key courses: Advanced Statistics, Machine Learning,Deep Learning, Data Mining and Big Data,
Data Analysis with Python.
Carreer Summary
ETH, Zurich – Research Assistant
Event Reconstruction for FASER Neutrino Detection
Developing event reconstruction techniques for the FASER experiment at CERN, integrating conventional algorithms and deep learning methods. FASERnu detects high-energy neutrinos produced at the LHC, covering a previously unexplored forward region. The project involves building reconstruction from scratch, addressing hit/event classification and noise suppression to enhance measurement precision and support future physics analyses.
CERN Summer Student – Openlab
CERN, Switzerland, Geneva
During the prestigious CERN Openlab summer student programme, I collaborated on cutting-edge projects in high-energy physics computing, gaining hands-on experience with the latest hardware and software technologies. As part of this programme, I refined muon track reconstruction in the barrel region at the Level-1 trigger for the CMS experiment, using the Run-3 data scouting demonstrator. I implemented deep learning algorithms optimized for FPGA deployment, enhancing the online reconstruction of muon parameters from stub-only data.
Research Assistant, CMS Experiment
Luleå University of Technology – University of Padua
Exclusively developed and implemented advanced pattern recognition algorithms using a neuromorphic computing model based on spiking neural networks (SNN) for the reconstruction of particle tracks from proton-proton (pp) collisions within the CMS experiment. Currently, in the process of preparing a paper to share the findings of this research.
Collaboration involving 7 students and researchers from around the world
INFN – Student Internship
I contributed to the JUNO collaboration by developing machine learning techniques to enhance event selection performance:
– Developed machine learning algorithms for neutrino interaction selection in the JUNO experiment.
– Optimized Boosted Decision Trees (XGBoost) and Deep Neural Networks for event classification.
– Enhanced accuracy in detecting reactor anti-neutrino events by reducing background noise.
– Achieved better performance compared to the state-of-the-art manual selection methods.
The work has been published on the University of Padova website: link.
Core Skills
Languages: C++, Python, JavaScript, Bash
Web Development: HTML, CSS
Miscellaneous: ROOT (open-source framework for high-energy physics), SSH, Machine Learning, Jupyter Notebook
Operating Systems: Linux (Implemented personal distribution based on Manjaro), MacOs, Windows
Language Proficiency
Italian: Native Speaker
English: Proficient (C1 Level, IELTS Certified)
French: Intermediate Level
Fabio Cufino 