Keynote: Constraint-Aware Generative Models

Abstract: Generative AI has recently attracted significant attention for its potential to accelerate a broad range of scientific and engineering domains. However, while these models produce statistically plausible outputs, they often fail to adhere to physical principles, conservation laws, or safety constraints. Such violations result in suggested designs that may be impractical, unstable, or even hazardous. This talk presents our current efforts to address these challenges by introducing a new class of training-free, constraint-aware diffusion models that integrate differentiable optimization techniques with generative modeling. We will review the mathematical foundations for incorporating both static and dynamic constraints into diffusion models, extend these results for the case of discrete diffusion models, and present case studies of inverse design in microstructural materials, protein-pocket design, multi-robot motion planning, and synthetic chemistry with safe and reliability constraints.

Short Bio: Ferdinando (Nando) Fioretto is an assistant professor of Computer Science at the University of Virginia. He leads the Responsible AI for Science and Engineering (RAISE) lab, whose research focuses on addressing foundational challenges to advance artificial intelligence, privacy, safety, and the intersection between machine learning and optimization for scientific applications. His work has been recognized with the 2025 DARPA disruptive ideas award, the 2022 Caspar Bowden PET award, the IJCAI-22 Early Career spotlight, and several best paper awards. Nando is also a recipient of the NSF CAREER award, the Google Research Scholar Award, the Amazon Research Award, and the ACP Early Career Researcher Award in Constraint Programming. He is a board member of the Artificial Intelligence Journal (AIJ) and an associate editor of the Journal of Artificial Intelligence Research (JAIR).