Ferdinando (Nando) Fioretto

I am an associate professor of Computer Science at the University of Virginia. I lead the Responsible AI for Science and Engineering (RAISE) group, where we develop foundational machine learning and generative AI methods that make models obey control objectives, hard constraints, safety requirements, and physical laws. Our work studies how to build controllable foundation models and constrained-aware generative models by connecting learning with optimization, control, and neuro-symbolic reasoning. My research has received a number of accolades, including best paper awards at leading journals and AI/ML conferences, industry research awards, fellowships, and investigators awards. See awards page for mode details.

Research

My works for on three key themes:

Foundational ML and Generative AI under Constraints: We develop the algorithmic foundations for predictive and generative models whose outputs are not only likely, but feasible, safe, and physically meaningful. This line of work builds on our earlier foundations for differentiable optimization, decision-focused learning, and learning-to-optimize systems, including Lagrangian methods for constrained deep learning (AAAI-20, ECML-20, AAAI-22), end-to-end prediction and optimization (IJCAI-22, JAIR-24, ECAI-24), and differentiable parametric programming and bilevel learning-to-optimize (CDC-24, L4DC-26). We now extend these ideas to constrained continuous diffusion and flow matching (NeurIPS-25a, ArXiv:26a), constrained discrete diffusion and search-augmented masked diffusion (NeurIPS-25b, ArXiv:26b), and constrained code generation and diffusion-based speculative decoding (ArXiv:26c, MLSys-26).

AI for Science and Engineering: We use these foundations to support scientific discovery and engineering design, including inverse design of protein binders (ICLR-26), materials with defects and micro-material design (AI4Mat-25, NeurIPS-24, NeurIPS-25a), safe multi-robot motion planning (ICML-25, AAAI-26), and molecular generation workflows (NeurIPS-25b, ArXiv:26). Our physics-informed and constraint-aware methods have also been used to accelerate energy optimization, operational planning, and scheduling tasks (e.g., AAAI-20, AAAI-22, NeurIPS-21, EPSR-23, NeurIPS-23, ICLR-25).

Responsible AI: We analyze the assurance of AI systems in support of decision-making and learning tasks, focusing especially on privacy, safety, and fairness. Our work has uncovered new theoretical and algorithmic understanding at the intersection of privacy and fairness, including fairness-aware post-processing of private data releases, differentially private and fair learning, and fairness issues in socio-demographic data processes (e.g., IJCAI-21, IJCAI-22, AAAI-21, AAAI-25). We also study how safety and fairness can emerge when machine learning and agentic LLMs pipelines are constrained by systems or robustness considerations, including pruning at initialization (NeurIPS-22), hardware failures and deployment choices (ICML-24), and adversarial robustness requirements (IJCAI-24).

For more details, please see our publications and my 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 example projects (a bit outdated now).

If you are interested in working with us, send me your CV and interests and make sure you check our latest work!

Research sponsors

Highlighted work

Constraint-Aware Diffusion for Scientific Applications

Teaching diffusion models to obey physics, geometry, and safety constraints — without retraining.

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Constrained Discrete Diffusion for Language, Chemistry, and Code

Enforcing hard constraints on tokens, molecules, and programs — training-free, inside the denoising loop.

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Differential Privacy in Artificial Intelligence Book

The most comprehensive and up-to-date resource on differential privacy for AI.

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Massively Speeding up LLM inference with Speculative Diffusion Decoding

Faster LLM inference with discrete diffusion drafting and alignment.

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