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 the 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

[News]: One PhD Positions in ML+Optimization is Available. Contact me if you are interested. : Click here to learn more

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News Highlights

Aug 2021 • Our NSF SaTC CORE proposal has been funded! Thank you, NSF!
The Differential Privacy for Fair Decision Making project page will be soon available.
CP 2021 I will give a Keynote Talk: "Constrained-based Differential Privacy".
June 2021 • New preprint "Differentially Private Deep Learning under the Fairness Lens".
• New preprint "Learning Hard Optimization Problems: A Data Generation Perspective".
• 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".
• New preprint "Decision Making with Differential Privacy under a Fairness Lens (Extended Version)".
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].
• New preprint "End-to-End Constrained Optimization Learning: A Survey".
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".
Oct 2020 PRIMA 2020 paper accepted: "The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach".
IEEE Transactions on Power Systems paper accepted: "Differentially Private Optimal Power Flow for Distribution Grids".
Aug 2020• Our NSF RI proposal has been funded! Thank you, NSF! Check the Deep Constrained Learning project page.
Jul 2020 • New preprint "High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow".
• New preprint "Differential Privacy of Hierarchical Census Data: An Optimization Approach".
Jun 2020 • New preprint "Differentially Private Convex Optimization with Feasibility Guarantees".
ECML 2020 paper accepted: "Lagrangian Duality for Constrained Deep Learning".
Apr 2020 IJCAI 2020 paper accepted: "Differential Privacy for Stackebelg Games".
IJCAI 2020 paper accepted: "OptStream Releasing Time Series Privately".
Apr 2020 PSCC 2020 paper accepted: "Privacy-Preserving Obfuscation for Distributed Power Systems".
Jan 2020 • New preprint "Bilevel Optimization for Differentially Private Optimization".
• I am excited to be joining the EECS department at Syracuse University.
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Selected Honors and Awards


    4-125 CST
    Department of Electrical Engineering & Computer Science
    Syracuse University
    Syracuse - NY 13244 - U.S.A.
    +1 (575) 621-5948