Ph.D., University of Udine and New Mexico State University B.S., University of Parma


Research interests include:

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


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.


  • 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

Research Interests

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