Career Profile
I’m a recently graduated Physicist (Master’s degree, University of Trento) with a strong passion for computer science. This passion led me to choose a computational path both for my curricular and extracurricular classes and to work on personal projects in my free time. I’m also one of the founding members of Progetto Apollo, a scientific divulgation group operating at the University of Trento in collaboration with Arditodesìo. Since 2019 I’ve been a tutor for the University of Trento. My hobbies include video games and parkour.
Education
Statistical Biophysics path with a focus on computational methods.
Thesis title: Computational Models Of Astrocyte-Neuron Dynamics To Explain The Formation Of Working Memory
Final grade: 110/110
Relevant courses:
- Quantum Computing and Quantum Machine Learning
- Advanced Computational Physics
- Experimental Methods
- Laboratory of Advanced Electronics (signal processing via FPGA)
- Multi-Scale Methods in Soft Matter Physics
- Statistical Mechanics and Statistical Field Theory.
Thesis title: Emergent features: from renormalization group to artificial intelligence
Relevant courses:
- Scientific Computing (fka Computer Science), Introduction to Machine Learning (fka Advanced Algorithms)
- Linear Algebra I, Analysis I, II, III, and Complex Analysis, Complementary Mathematics for Quantum Mechanics
- Physics I, II, and III, Analytical Mechanics, Quantum Mechanics
- Physics Laboratory I (measures), II (analogue electronics and optics), and III (digital electronics)
- Chemistry with Laboratory Sessions.
- CyberChallenge.IT (March-June 2021): during my master’s degree, I took part in the CyberChallenge.IT, a course about cybersecurity held by the Cybersecurity National Lab in collaboration with the Department of Information Engineering and Computer Science.
- Data Science for Physicist (March-June 2020): after my bachelor’s degree, I took part in the course Data Science for Physicist as a listener.
- Emozionare con la Scienza: during my bachelor’s degree, I took part in a Scientific Storytelling course twice. This resulted in two 5-minute plays and the creation of the storytelling group Progetto Apollo.
Experiences
My job was to explain the lab experiences to the students in order to make sure they understood the underlying reasoning and the scientific approach. I was assigned to the following courses:
- Sep 2022 - Dec 2022: Introductory Physics Laboratory (DII), Mathematical bases for Cognitive Sciences (CIMEC), Mathematics and Statistics I (CIBIO)
- Mar 2022 - June 2022: Laboratory of Physics of Matlab (DIF), Introductory Physics Laboratory (DICAM)
- Set 2021 - Dec 2021: Introductory Physics Laboratory (DII)
- Set 2020 - Dec 2020: Mathematics and Statistics (A3C)
- Set 2019 - Jan 2020: Calculus I (DICAM), Quantitative methods for life-sciences (DIPSCO).
I'm one of the founding members of Progetto Apollo, a scientific divulgation group operating at the University of Trento in collaboration with Arditodesìo. Some of our activities include research on a key topic or an interview with a Professor at the University of Trento to write narratives with the principles of storytelling. The narratives were then performed in the course of various editions of Teatro della Meraviglia, EIT RAW Materials 2021, CO.SCIENZA Festival 2019, Focus Days 2019 and Fisicittà 2017. In 2022 we partnered with *Caltech* to write four narratives presented both in Trento and at Caltech.
Projects
It is possible to find other projects on Github.
Percolation - Theory and Applications
- For the course Multi-Scale Methods in Soft Matter Physics I wrote a review that introduces the reader to the Bernoulli percolation model and its phase transition. The review also presents attempts to apply Bernoulli percolation to physical problems and a proof of concept for simulation based on the Monte-Carlo method.
Machine Learning Phases of Matter
- This project aims to reobtain the results presented in Machine learning phases of matter (Juan Carrasquilla, Roger G. Melko) regarding the two-dimensional square Ising model. The authors show that a standard feed-forward neural network (FFNN) is a suitable tool to detect Ising's model phase transition.
A Game of Life
- Developed for the course Laboratory of Advanced Electronics including my final project that implements Conway's Game of Life on an FPGA and outputs the results using the VGA standard.
Practical Deep Learning for Coders
- I'm currently taking fast.ai's course Practical Deep Learning for Corders.