Ferdinando (Nando) Fioretto

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 with differentiable optimization and neuro-symbolic reasoning for complex scientific and engineering problems.
Our constrained-aware methods are being applied to support the inverse design of proteins 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). Additionally, our physics-informed methods have been used to massively accelerate energy optimization and operational planning as well as 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.

Our 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.

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 See this page for example projects (a bit outdated now).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

Constrained-aware Diffusion for Scientific Applications

Constrained-aware Diffusion for Scientific Applications

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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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