At TU Dortmund, I design and implement cache-efficient compressed text indexes in C++, while investigating fast, space-efficient text compression algorithms. I also coordinated an 11-student master project group on algorithm visualisation and supervised Bachelor theses.
At the University of Verona, I introduced heuristics for faster text index construction on large datasets and designed dynamic data structures for permutations and string collections, implementing both in C++. My PhD received the Best Italian PhD Thesis in Theoretical Computer Science 2025 award from the EATCS Italian Chapter.
I’ve worked on applied machine learning, including an HMM-based anomaly detection system for industrial sensors and an adversarial data augmentation strategy for out-of-distribution robustness. I also developed CRISPRme, a Python/Dash web application for genome-wide CRISPR off-target search.
My research spans algorithms and data structures, text processing, time-series analysis, and machine learning pipelines. My publications include work on time-series distances, anomaly detection, text indexing, and forecasting.
