Ferdinando (Nando) Fioretto
Assistant Professor of Computer Science, University of Virginia
307 Rice Hall
85 Engineer's Way
Charlottesville, VA 22904
I am an assistant professor of Computer Science at the University of Virginia.
I lead the Responsible AI for Science and Engineering (RAISE) group where we make advances in artificial intelligence with focus on two key themes:
Foundation Models for Science and Engineering: We develop the foundations to blend predictive and generative models and neuro-symbolic reasoning for complex scientific and engineering problems.
Responsible AI: We analyze the assurance of AI systems in support of decision-making and learning tasks, focusing especially on privacy, safety, and fairness.
My research has been generously supported by various projects from the National Science Foundation, a Google Faculty Research Award, an Amazon Research Award, a Cohere For AI Research Grant Program, a NVIDIA Academic Grant Program Award, the LaCross Institute, and the University of Virginia.
For more details, please see my papers and short bio
Opportunities for Undergaduates Students
I regularly accept undergaduates and graduates interns who are willing to work on Responsible AI or Differentiable Optimization topics. See this page for current projects available.
If you are interested in working with us, send me your CV and interests and make sure you check our latest work!
Research sponsors
Our group is grateful for the generous support from our sponsors:
Highlighted work
A few recent projects that reflect our current directions and collaborations.
Differential Privacy in Artificial Intelligence Book
The most comprehensive and up-to-date resource on differential privacy for AI.
Learn more
Speculative Decoding with Discrete Diffusion
Faster LLM inference via SpecDiff and SpecDiff-2.
Learn moreRecent News
38 total-
Feb 2026
Publication- New preprint on Masked Diffusion Models + Constraints! The idea uses search augemented scheme during the reverse denoising step of diffusion to optimize for constraint violations: Search-Augmented Masked Diffusion Models for Constrained Generation .
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Jan 2026
Publication- Two ICLR 2026 papers accepted, on Constrained Diffusion for Protein Design and on Decision-Focused Generative Learning. Congrats to Jacob and Jinhao and co-authors!
- Our SpecDiff-2 work has been accepted at MLSys 2026! Congrats to Jameson and Jacob and co-authors!
- Paper on Learning to optimize in Bilevel Optimization accepted to L4DC 2026. Congrats to James and co-authors!
- New preprint on privacy guardrails: NeuroFilter: Privacy Guardrails for Conversational LLM Agents.
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Dec 2025
Publication- Paper accepted at the IEEE Transactions on Smart Grid on Decision focused learning with neural ODEs for proactive grid resilience management.
- Paper accepted at IEEE SatML 26 on Data Minimization.
- Our work Constrained Molecular Generation with Discrete Diffusion was awarded the Best Student Paper Award at the NeurIPS AI4D3 Workshop! Congrats to Jacob and Michael!
- I gave a talk at the NeurIPS COML Workshop on Generative AI for Scientific and Engineering Research.
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Nov 2025
Publication- Our work Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning was accepted at AAAI 2026! Congrats to Jinhao!
- Check out our new work on accelerating LLM inference with discrete diffusion models! These results are amazing!
- I gave a talk at the ORNL AI Core workshop on Generative AI for Science.
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Oct 2025
Publication- Check out our new work on manifold constrained protein design with diffusion models!.
- Several new preprints on topics spanning from physics-constrained generative flow matching and diffusion models to safety in agentic AI systems and learning to solve PDE-constrained problems.
Check out our publications’ page for details.
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Sep 2025
Publication- Two papers on physics-constrained generative diffusion models (as Spotlight) and logic-constrained language diffusion models accepted at NeurIPS 2025
Congratulations to Jacob, Michael, and Stefano!
- Our book Differential Privacy in Artificial Intelligence: From Theory to Practice is now online, available as Open Access!
This book covers the (i) theoretical underpinnings of Differential Privacy, (ii) recent algorithmic advances for machine learning,, (iii) practical applications across key engineering and scientific domains, (iv) methodologies for implementation and empirical evaluation, and (v) the surrounding legal and ethical frameworks.
The book is also available on Amazon and Barnes & Noble. -
Aug 2025
Publication- New preprint: Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning. We provide a new integration of discrete optimization and diffusion models for multi-robot motion planning scaling to 100 of agents in cluttered enviroments.
- New preprint: Stability-Constrained AC Optimal Power Flow–A Gaussian Process-Based Approach. We propose a new, scalable approach to incorporate generator dynamics into the ACOPF using Gaussian Process (GP) models. Crucially, this enables probabilistic stability assessment to be integrated directly into the optimization process!
- NSF-RI proposal on Generative Models for Scientific Exploration funded! We’ll integrate optimization and physical principles dirctly in generative models for a variety of scientific applications! Thank you, NSF!
- Paper Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming accepted at CDC-2025!
- New preprint: SoK: Data Minimization in Machine Learning. We provide introduces a comprehensive framework for data minimization in machine learning and introduce a systematical review the literature on data minimization and adjacent methodologies.
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Jul 2025
Award- Congratulations to Michael Cardei for winning an NSF GRFP Scholarship Award!
- I gave a talk at USNCCM18 about our generative AI for material discovery. -
Jun 2025
Publication- New preprint: Disclosure Audits for LLM Agents. We provide a model to audit disclosure risks for LLM agentic systems with conversational interfaces.
- Our paper on Neuro-symbolic Generative Diffusion was awarded a DARPA Disruptive Idea Award! Thank you, DARPA for the support!
- We received two 4-VA research grants! We will work with George Mason and Virginia Tech to build more aligned and interpretable LLM models and more robust learning to optimize pipelines. Thank you 4-VA for your support! -
May 2025
Publication- Grateful to receive research credits from Google Cloud! We will develop more secure agentic LLM systems . Thank you, Google!
- Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models was accepted at ICML 2025. Congraulations to Jinhao and Jacob!
- Neuro-symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation was accepted at Neu 2025.
Congratulations to Jacob, Michael, and Jinhao!
- Congratulations to Joon Ko, who was awarded an Excellence in Research Award for undergraduate research at UVA CS! -
Apr 2025
Publication- Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion was selected for an Oral presentation to NAACL 2025. Congraulations to Jacob and Michael!
- Our paper The Data Minimization Principle in Machine Learning was accepted at FAccT 2025. Congratulations to Prakhar!
- Congratulations to Michael Cardei, who was awarded an NSF GRFP Honorable Mentions! -
Mar 2025
Award- Greatful to receive an NVIDIA Academic Grant Program Award! We will develop faster LLM inference. Thank you, NVIDIA!
- Excited to share our lab’s new work: Constrained Language Generation with Discrete Diffusion Models!
- Our paper Low-rank finetuning for LLMs: A fairness perspective was awarded a Best paper award at the AAAI 2025 colorai workshop.