Ferdinando Fioretto

About

Ferdinando Fioretto works on machine learning, optimization, differential privacy, and fairness. His recent work focuses on (1) the integration of constrained optimization and machine learning to enhance the expressive ability of machine learning models and (2) understanding the interplay among privacy, equity, robustness, and performance in machine learning models and decision tasks. He is a recipient of the Amazon Research Award, the NSF CAREER award, the Google Research Scholar Award, the Caspar Bowden PET award, the ISSNAF Mario Gerla Young Investigator Award, the ACP Early Career Researcher Award, the AI*AI Best AI dissertation award, and several best paper awards.

Research interests include:

Deep Learning, Optimization, Responsible AI, Differential Privacy, Algorithmic Fairness

Education

Ph.D., University of Udine and New Mexico State University

B.S., University of Parma