Summary
Marco Minici is a researcher and computational scientist with a decade of experience building ML tools to detect online harms—from malicious actors to unpredictable recommender algorithms. Currently at ICAR-CNR and with a recent visiting stint at USC ISI, he combines academic rigor (PhD in AI & Society) with industry experience in personalization at Amazon Music and applied ML projects across security and forecasting. His work blends recommender systems, knowledge graphs and content enrichment to make sense of information overload and study algorithmic radicalization. Notably, he has applied production ML practices (PyTorch, Spark, Docker) to both industrial problems and reproducible research, and has defended a thesis that bridges opinion dynamics with practical detection frameworks. Based in Italy, he is fluent at translating complex simulation and causal questions into deployable tools for platforms and policymakers.
10 years of coding experience
1 year of employment as a software developer
Master's Degree, Data Science, 110, Master's Degree, Data Science, 110 at Sapienza Università di Roma
Laurea Triennale, Computer Engineering, 104/110, Laurea Triennale, Computer Engineering, 104/110 at Università degli Studi della Calabria