I'm currently a Master's student in the "Physics of Data" program at the Università degli studi di Padova.

• 🎓 Education: Master’s in Physics of Data, Università degli studi di Padova
• 🧪 Focus Areas: Scientific computing, data analysis, data visualization, interdisciplinary physics
• 🐍 Coding: Primarily Python, with some experience in R and minor experience in Julia, C++ and Matlab
• 🌐 Interests: Complex Networks and their diverse real-world applications

• Car make classification and verification using Snapshot Ensembling: Final project for the course Neural Networks and Deep Learning. We train a model for car make classification and verification based on images from the CompCars dataset and use snapshot ensembling to improve performance without incurring additional training cost.
• Batch Analysis of Cosmic Rays Using Drift Tubes Detectors: Final project of the course Management and Analysis of Physics Datasets Mod. B. Implemention of a pipeline for the efficient reconstruction of muon trajectories crossing through drift chambers at the INFN center in Legnaro (PD); leveraging parallel processing on a cluster with Dask.
• Transformers: Analysing the transformation of token representations in different layers of GPT-2: Final project for the course Laboratory of Computational Physics Module B. Analysis of token representations extracted at different layers of the GPT-2 model in terms of their intrinsic dimension and pairwise distances. Additionally, the effect of different parameters on the token probabilities after the last model layer are explored.
• Binary star evolution and binary black holes: Final project for the course Laboratory of Computational Physics Module A. Goal: differentiating binary black hole systems that evolved via stable mass transfer (MT) from those who evolved via common envelope (CE) by training a Random Forest model on simulation data and identifying the system features with the highest impact on the evolution regarding MT/CE.
• Quantum Principal Component Analysis: Final project of the course Quantum Information and Computing. End-to-end implementation of Quantum PCA using qiskit.
• Water Quality in Padova's Canals: Final project of the course Modelling and Control of Environmental Systems. Modelling the Dissolved Oxygen dynamics in Padova's canals based on Water temperature & Solar irradiance in matlab, performing model calibration via particle swarm optimization.
• Learning the topology of a Bayesian Network from a database of cases using the K2 algorithm: Final project of the course Advanced Statistics. R Implementation of the K2-algorithm to construct Bayesian belief-networks from records, and application to 3 small test datasets and one real-world dataset.

• Community structures in complex networks via maps of random walks: Final project for the course Information Theory and Inference. Decomposing complex networks into modules by compressing a description of the probability flow of random walks on the network.
• Complex Networks projects: End-of-semester projects for the course Physics of Complex Networks: Structure and Dynamics
  • Robustness of noisy Quantum networks: Simulating and analysing the robustness of Quantum networks based on Erdős–Rényi and Barabási-Albert structures.
  • European Transportation Network : Reconstruct and analyse the rail networks for different EU countries, based on the provided network data.

Repositories containing weekly homeworks and assignments for different courses:

• Physical Models of Living Systems. Topics: Ecological modelling (Population dynamics of single/multiple species, species interaction), Computational Neuroscience (Firing rate network models, Hopfield networks, more to be added)
• Information Security. Topics: Linear cryptanalysis, block ciphers, WTC and random binning encoding, authentication and integrity protection, key agreement schemes
• Quantum Information and Computing. Topics: Quantum harmonic oscillator, Quantum Ising Model, Imaginary time evolution for ground state search, Real Space Renormalization Group, Infinite Density Matrix Renormalization Group.
• Laboratory of Computational Physics, Mod. B. Topics: CNNs, Clustering, XGBoost, Restricted Bolzmann Machines.
• Laboratory of Computational Physics Mod. A. Topics: Introduction to scientific computing with Python, data analysis and visualization