MEng Student in AI at University of Bologna, research assistant at USTC

I am a graduate student in Artificial Intelligence (Engineering) at the University of Bologna, with a strong interdisciplinary background spanning statistics, machine learning. I’m also attending a parallel (second) undergraduate degree  in IT. My academic path combines solid theoretical foundations with hands-on research and industry experience, with a particular focus on probabilistic modeling, data-driven methods, and ML architectures.

I previously graduated with honours in Statistics, Finance and Insurance, completing a thesis on Bayesian Hierarchical Models for areal data (BYM), and later pursued a second bachelor’s degree in Information Technology for Management. Alongside my studies, I have taken part in international exchange and visiting student programs at institutions including Yonsei University, Stockholm University, and the National University of Singapore, attending graduate-level courses in generative AI, distributed learning, and related topics and always mantaining a high GPA.

My professional experience includes research internships at the Italian National Research Council (CNR), where I worked on Fourier Neural Operators and deep learning methods for PDEs, as well as industry experience as a Data Analyst Intern at Munich Re, focusing on large-scale data auditing for financial positions monitoring. I will further develop my research profile through upcoming internships at USTC (China) in AI for modeling and control, and at Leonardo S.p.A., where my master’s thesis will focus on hybrid ML–deterministic algorithms for adaptive radar systems.

Technically, I work primarily with Python, R, SQL, and Java, and I am comfortable with modern ML frameworks (e.g. PyTorch), numerical modeling, and reproducible research workflows. I am motivated by research problems at the intersection of statistics, AI, and real-world systems, especially where uncertainty quantification and methodological rigor matter.