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) and Syracuse University.

I received a PhD in Computer Science from the University of Udine (with dual degree from New Mexico State University). My doctoral dissertation titled "Exploiting the Structure of Distributed Constrained Optimization" received a AI*IA award for best AI dissertation.
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. 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

The Third AAAI Workshop on Privacy Preserving Artificial Intelligence

Together with Pascal Van Hentenryck and Aleksandra Korolova, I am organizing the third workshop on Privacy Preserving Artificial Intelligence (PPAI) at AAAI-22.
The aim of the workshop is to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI.

Links: PPAI-22 website | AAAI-22 website


The AAAI Workshop on Machine Learning for Operations Research ML4OR-22

Together with Emma Frejinger, Elias B. Khalil, and Pashootan Vaezipoor, I am organizing the first AAAI Workshop on Machine Learning for Operations Research (ML4OR) at AAAI-22.
The workshop builds on the momentum that has been directed over the past 5 years, in both the OR and ML communities, towards establishing modern ML methods as a “first-class citizen" at all levels of the OR toolkit. ML4OR will serve as an interdisciplinary forum for researchers in both fields to discuss technical issues at this interface and present ML approaches that apply to basic OR building blocks (e.g., integer programming solvers) or specific applications.

Links: ML4OR-22 website | AAAI-22 website


Other recent News and Highlights

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 "End-to-end Learning for Fair Ranking Systems".
• 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".
• New preprint "Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method".
Sep 2021 • New preprint "A Fairness Analysis on Private Aggregation of Teacher Ensembles".
• I will be co-chairing 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 will give 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 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