Rice Hall Room 307
85 Engineer's Way
Charlottesville, VA 22903
Google Scholar Github Page


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


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

B.S., University of Parma

Research Interests

Deep Learning
Responsible AI
Differential Privacy
Algorithmic Fairness


ICLR Notable Reviewer Award 2023
NMSU CS Star Award 2023
Amazon Research Award - AWS AI 2022
Caspar Bowden PET Award 2022
IJCAI Early Career Spotlight 2022
Google Research Scholar Award 2022
NSF CAREER Award 2022
IEEE Transaction of Power System Best Paper Award 2022
NeurIPS Outstanding Reviewer Award 2021, 2022
ISSNAF Mario Gerla Young Investigator Award 2021
ACP Early Career Research Award 2021
IEEE Transaction of Power System Best Paper Award 2021
AI*IA 2017 Best AI Dissertation Award 2018
AAMAS 2016 Visionary Workshop Paper Award 2016
CMSB 2013 Best Student Paper Award 2013