Ferdinando Fioretto

Assistant Professor of Computer Science, University of Virginia

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307 Rice Hall

85 Engineer's Way

Charlottesville, VA 22904

I am an assistant professor of Computer Science at UVA. I lead the Responsible AI for Science and Engineering (RAISE) group where we make advances in artificial intelligence with focus on two key themes:
AI for Science and Engineering: We develop the foundations to blend deep learning and constrained optimization for complex scientific and engineering problems.
Trustworthy & Responsible AI: We analyze the equity of AI systems in support of decision-making and learning tasks, focusing especially on privacy and fairness.

My group is generously supported by various projects from the National Science Foundation, Google, Amazon, and the University of Virginia.
Before joining the University of Virginia, I was an assistant professor at Syracuse University. Prior to that 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.

Selected Talks

Toc 4 Fairness Seminar (Nov 23) ACP Summer School 2023 (Aug 23) DTU Summer School 2023 (Jun 23) FAccT 2022 Tutorial (June 22) CP 2021 Invited Talk (Nov 21) ACP Early Career Award (Dec 21)

news

May-24 - I am co-organizing the first Summer School in AI and OR (AISCORE).
- Two papers accepted to ICML 2024! On The Fairness Impacts of Hardware Selection in Machine Learning, and Disparate Impact on Group Accuracy of Linearization for Private Inference. :sparkles:
- I gave a talk on Disparate impacts of compression in Machine Learning at BuzzAI.
Apr-24 - Our paper End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty has been accepted to UAI 2024! :sparkles:
- I gave a talk on formalizing the principle of data minimization for Machine Learning at the Google Privacy seminar serires.
- Our paper Fairness Increases Adversarial Vulnerability has been accepted to IJCAI 2024! :sparkles:
- Our paper Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages has been accepted to FAccT 2024! :sparkles:
- New preprint: Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming ! :sparkles: See publications for details.
Mar-24 - New preprint: Learning Constrained Optimization with Deep Augmented Lagrangian Methods! :sparkles: See publications for details.
- The fith edition of the Privacy Preserving AI workshop at AAAI-24 has been an exciting event. Check out the website for information about its program!
- Check out my piece on Fairness in The conservation.
Feb-24 - New preprint: End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty! :sparkles: See publications for details.
- New preprint: Projected Generative Diffusion Models for Constraint Satisfaction! :sparkles: See publications for details.
- New preprint: Disparate Impact on Group Accuracy of Linearization for Private Inference! :sparkles: See publications for details.
- New preprint: Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages! :sparkles: See publications for details.
- I gave a talk at Amazon Science on the disparate impacts of private machine learning.
Jan-24 - New preprint: Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization! :sparkles: See publications for details.
Dec-23 - Paper accepted to AAAI 2024! :sparkles: See publications for details.
- New preprint: On The Fairness Impacts of Hardware Selection in Machine Learning! :sparkles: See publications for details.
- :speech_balloon: I am co-organizing the NeurIPS Algorithmic Fairness through the Lens of Times workshop. Hope to see you in NOLA on December 15, 2023!
Nov-23 - New preprint on integrating prediction and optimization via proxy learning! :sparkles: See publications for details.
- :speech_balloon: I will give talks on the Ethical AI at Toc4Fairness and a keynote at the Comète Workshop on Ethical AI.
- Our paper on disparate impacts arising in energy optimization has been accepted to the NeurIPS 2023 Climate Change AI Workshop! :sparkles:
Oct-23 - :speech_balloon: I will give talks on the integration of Optimization and Deep Learning at UVA, INFORMS-23, and CCS-23.
Sep-23 - Our paper on data minimization at inference time has been accepted to NeurIPS 2023! :sparkles: See publications for details.
- I am co-organizing the fith edition of the Privacy Preserving AI workshop at AAAI-24. :sparkles: This year edition will have a particular focus on privacy in generative models. Stay tuned!
- Paper accepted in IEEE PES Innovative Smart Grid Technologies. :sparkles: See publications for details.
Aug-23 - I gave a talk on Privacy and Fairness at the IJCAI-23 workshop on Deep Learning Methods for Social Media.
- New survey on integrating prediction and optimization in end-to-end differentiable systems! :sparkles: See publications for details.
- I am co-organizing the Algorithimc Fairness through the Lens of Time workshop at NeurIPS to spark discussions on how a long-term perspective can help build more trustworthy algorithms in the era of generative models.
- New preprint on the disparate impacts arising in energy optimization. :sparkles: See publications for details.
- :star2: I have moved to the University of Virginia.
Jul-23 - I gave a talk about the integration of Machine Learning and Optimization at the 2023 ACP Summer School. :speech_balloon: Youtube link
Jun-23 - Paper accepted to Electric Power Systems Research! :sparkles: See publications for details.
- I gave a talk about Differential Privacy, Foundation and applications in Energy Systems at the DTU PET Summer School. :speech_balloon: Slides.
- I gave a talk about Machine Learning for Optimization Optimization at the IEEE PES University.
May-23 - Two new exciting preprints on privacy and fairness. :sparkles: See publications for details.
- :tada: Congratulation to Dr. Cuong Tran on a stellar defense! You can check his work here!
- Our NSF-ENG EPCN proposal Physics Informed Real-time Optimal Power Flow has been funded! We’ll integrate machine learning and physics to optimize power flow, integrate systems dynamics, and increase grid reliability. We’ll work on scalable and robust ML for energy solutions. Thank you, NSF!
Apr-23 - :trophy: Notable reviewer award ICLR 2023 (link).
- Four papers accepted to IJCAI 2023! :sparkles: See publications for details.
Mar-23 - Our NSF RI CORE proposal End-to-end Learning of Fair and Explainable Schedules for Court Systems. has been funded! We’ll develop differentiable optimization tools for equitable & explainable scheduling and work on changing the pretrial scheduling process to reduce nonappearance and promote fairness in the American Court system! Thank you, NSF!
- I am co-editing a new book :open_book: on Differential Privacy . It covers topics from foundations, to applications in optimization (including in Census data release, image and video, and medicine), machine learning (including privacy attacks, federated learning, and private algorithms) as well as policy and ethics aspects. Stay tuned!
Feb-23 - I have co-organizing the fourth workshop on Privacy Preserving Artificial Intelligence (PPAI) at AAAI-23.
Jan-23 - 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. :sparkles: :sparkles: See publications for details.
- Paper accepted to IEEE PES 2023! :sparkles: See publications for details.
- I gave a talk about Differential Privacy and Fairness in Energy Systems at the Grid Science winter school.
- 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! :sparkles: See publications for details.