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

My work makes advances in foundational Artificial Intelligence (AI) research with focus on two key themes:

Scientific AI. It develops the foundations to blend deep learning and combinatorial optimization to serve the resolution of complex scientific and engineering applications and creates novel ways to integrate knowledge, constraints, and physical principles into learning models.
Responsible AI. It analyzes the equity of AI systems in support of decision-making and learning tasks, and it designs practical algorithms to make AI systems more aligned with societal values, focusing especially on privacy and fairness.

I approach these directions by applying the tools and perspectives of optimization theory, differential privacy, and statistics to problems in decision-making and machine learning. For more details, please see my publications.

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

I am a recipient of the 2022 AWS Amazon Research Award, the 2022 NSF CAREER award, the 2022 Google Research Scholar Award, the 2022 Caspar Bowden PET 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 (Italy) and a BS in Computer Science and Mathematics from the University of Parma (Italy). For more details, please see my CV.

Prospective Students

Due to the large number of requests I receive, unfortunately, I cannot respond to all prospective graduate students. I apology in advance. It is best to contact me after you have submitted your application.
Occasionally, I post openings for various roles (research scholars, interns, etc.). Please check my website or follow me on Twitter for updates.

Curriculum Vitae Google Scholar DBLP GitHub Twitter

Selected Talks

CP 2021 Invited Talk (Nov 21) ACP Early Career Award (Dec 21) FAccT 2022 Tutorial (June 22)

News Highlights

The 4th AAAI Workshop on Privacy Preserving Artificial Intelligence

Together with Pascal Van Hentenryck and Catuscia Palamidessi, I am organizing the fourth workshop on Privacy Preserving Artificial Intelligence (PPAI) at AAAI-23.
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-23 website | AAAI-23 website

The 14th Workshop on Optimization and Learning in Multiagent Systems

I am co-organizing the 14th workshop Optimization and Learning in Multiagent Systems (OptLearnMAS) at AAMAS-23.
This workshop invites works from different strands of the multi-agent systems community that pertain to the design of algorithms, models, and techniques to deal with multi-agent optimization and learning problems or problems that can be effectively solved by adopting a multi-agent framework. OptLearnMAS aims to provide a forum for researchers to discuss common issues that arise in solving optimization and learning problems in different areas, to introduce new application domains for multi-agent optimization techniques, and to elaborate common benchmarks to test solutions.
Consider submitting your best work!

Jan 2023 Four new exciting preprints on topics including differentiable optimization, data leakage in ML models, differential privacy in language models, and differentially private data disclosure methods. See publications for details.
Paper accepted to IEEE PES 2023 ! See publications for details.
I gave a talk about Differential Privacy and Fairness in Energy Systems at Grid Science
I will be serving as an area chair for FAccT-23, and ECAI-23.
I will be serving as demo track co-chair for IJCAI-23 and scholarship co-chair for AAMAS-23.
Paper accepted to AAMAS 2023 ! See publications for details.
Dec 2022 I was a panelist in the Algorithmic Fairness and its Intersections at NeurIPS.
I gave a tutorial on End-to-end constrained optimization learning at AIxIA 2022
Nov 2022 New preprint "Fairness Increases Adversarial Vulnerability".
I am co-organizing the Privacy Preserving Artificial Intelligence workshop at AAAI 2023.
Submission Deadline: Nov 21. Submit your best work!
Oct 2022 New preprint "End-to-End Optimization and Learning for Multiagent Ensembles".
New preprint "Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming".
I gave a tutorial on End-to-end constrained optimization learning at Dagstuhl
Sep 2022 Paper accepted to NeurIPS 2022 ! See publications for details.
I am honored to receive an Amazon Research Award (AWS) on Fairness. Thank you, Amazon!
Aug 2022 I am co-organizing the Algorithmic Fairness through the lens of Causality and Privacy workshop at NeurIPS2022. Submission Deadline: Sep 22. Submit your best work!
Jul 2022 Our paper "Decision Making with Differential Privacy under the Fairness Lens” received the 2022 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies.
Jun 2022 Check out the slides of our FAccT 2022 Tutorial on "Impacts of Data Privacy and Equity on Public Policy".
New preprint "Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support".
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 to 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 TPS 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 to 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/
I was a panelist in the DC Career Panel at AAAI-22
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".
I gave a keynote talk about Differential Privacy and Fairness at CP 2021
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.

Selected Honors and Awards


    4-125 CST
    Department of Electrical Engineering & Computer Science
    Syracuse University
    Syracuse - NY 13244 - U.S.A.