I am assistant professor in the EECS department at Syracuse University.

I work on artificial intelligence, privacy, and machine learning. My recent work focuses on (1) how to make AI algorithms better aligned with societal values, especially privacy and fairness, and (2) how to use machine learning for solving complex optimization problems. I study these questions using methods and models from optimization, differential privacy, and multiagent systems. For more details, please see my publications.

My research has been generously supported by the National Science Foundation (NSF), Google, and Syracuse University.

I am a recipient of the 2022 NSF CAREER award, the 2022 Google Research Scholar Award, the 2021 ISSNAF Mario Gerla Young Investigator Award, the 2021 ACP Early Career Researcher Award, the 2018 AI*AI Best AI dissertation award, and several best paper awards. Before joining Syracuse University, I was a postdoctoral research associate at the Georgia Institute of Technology and a research fellow at the University of Michigan. I received a PhD in Computer Science from the University of Udine (with dual degree from New Mexico State University). For more details, please see my CV.

Prospective Students

Please read this information before contacting me.

Curriculum Vitae Google Scholar DBLP GitHub Twitter

News Highlights

May 2022 Honored to be invited to deliver an Early Career Spotlight Talk at IJCAI 2022. See accompanying paper for details.
Apr 2022 I am honored to receive a Google Research Scholar Program award on Privacy. Thank you, Google!
Two papers accepted at IJCAI 2022 (see publications for details).
New preprint "SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles".
Our paper "Differentially private optimal power flow for distribution grids" won the 2022 Best IEEE TSP award!
Mar 2022 I have received an NSF CAREER award: CAREER: End-to-end Constrained Optimization Learning. Thank you, NSF!
Together with Claire Bowen I will be giving a tutorial on Differential Privacy and Fairness at FAccT 2022.
Two papers accepted at PMAPS.
Feb 2022 New preprint "Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey".
New preprint "Deadwooding: Robust Global Pruning for Deep Neural Networks".
I gave a talk about Differential Privacy and Fairness at https://prisec-ml.github.io/
Jan 2022 WWW 2022 paper accepted: "End-to-end Learning for Fair Ranking Systems".
New preprint "Post-processing of Differentially Private Data: A Fairness Perspective".
Dec 2021 I was awarded the 2021 Mario Gerla Young Investigator Award by ISSNAF
AAAI 2022 paper accepted: "Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method".
Nov 2021 I was selected as the recipient of the ACP Early Career Research Award.
I was awarded the Outstanding Reviewer award by NeurIPS 2021.
New preprint "Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions".
Oct 2021 NeurIPS 2021 paper accepted "Differentially Private Deep Learning under the Fairness Lens".
NeurIPS 2021 paper accepted "Learning Hard Optimization Problems: A Data Generation Perspective".
Sep 2021 New preprint "A Fairness Analysis on Private Aggregation of Teacher Ensembles".
I co-chaired two AAAI workshops: The workshop on Privacy Preserving AI: PPAI-22 and the workshop on Machine Learning for Operations Research: ML4OR.
Aug 2021 Our NSF SaTC CORE proposal has been funded! Privacy and Fairness in Critical Decision Making. Thank you, NSF!
CP 2021 I gave a Keynote Talk: "Constrained-based Differential Privacy".
June 2021 Our CUSE IIR proposal has been funded. "On the Potential Perils of Fairness Algorithms in Decision Making and Learning Tasks" with Sucheta Soundarajan.
New preprint "A Privacy-Preserving and Trustable Multi-agent Learning Framework".
May 2021 IJCAI 2021 paper accepted: "Decision Making with Differential Privacy under the Fairness Lens".
IJCAI 2021 journal track paper accepted: "Differential Privacy of Hierarchical Census Data: An Optimization Approach".
Apr 2021 IJCAI 2021 paper accepted: "End-to-End Constrained Optimization Learning: A Survey".
Mar 2021 Our paper "Privacy-preserving power system obfuscation: A bilevel optimization approach received the 2020 TPWRS Best Paper Award (IEEE Transaction of Power System) [awarded to 7 out of all papers published in 2018-2020].
Feb 2021 AIJ paper accepted: "Differential privacy of hierarchical Census data: An optimization approach".
Jan 2021 New preprint "Load Embeddings for Scalable AC-OPF Learning".
AAMAS 2021 paper accepted: "A Privacy-Preserving and Accountable Multi-agent Learning Framework".
Dec 2020 AAAI 2021 paper accepted: "Bias and Variance of Post-processing in Differential Privacy".
AAAI 2021 paper accepted: "Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach".
Aug 2020 Our NSF RI proposal has been funded! Thank you, NSF! Check the Deep Constrained Learning project page.
Jan 2020 I am excited to be joining the EECS department at Syracuse University.
More News

Selected Talks

CP 2021 Invited Talk (Nov 21) ACP Early Career Award (Dec 21) PriSecML Seminar (Feb 22)

Selected Honors and Awards

Contacts

Address:
    4-125 CST
    Department of Electrical Engineering & Computer Science
    Syracuse University
    Syracuse - NY 13244 - U.S.A.
Email:
    ffiorett@syr.edu
Tel:
    +1 (575) 621-5948