307 Rice Hall
85 Engineer's Way
Charlottesville, VA 22904
I am a Copenhaver Fellow and 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. We achieve these goals by connecting learning with optimization, control, and neuro-symbolic reasoning. My research has received a number of honors and accolades, including best paper awards at leading journals and AI/ML conferences, industry research awards, fellowships, career awards, and young investigator awards. See the awards page for more details.
Research Focus
- Foundational ML and Generative AI under Constraints Differentiable optimization and the integration of optimization with machine learning and generative AI.
- AI for Science and EngineeringGenerative methods for protein and molecular design, materials science, robotics, energy systems, chip design, and policy optimization.
- Discrete Diffusion Language ModelsFoundations for constrained discrete generation and its use in speculative technologies for speeding up language generation.
- Decision Focused LearningTraining predictive and generative models to optimize downstream decision quality, rather than prediction accuracy alone.
- LLM Multiagent SystemsFoundations for distributed coordination and problem solving and Safety
- Responsible AIAssurance for learning and decision systems, with an emphasis on privacy, safety, fairness, and robustness.
I am looking for self-motivated PhD students, postdocs, and interns!
Students should have a strong background in generative models and optimization and a strong interest in at least one of the following research areas:
- Foundations of constraint-aware generative AI
- GenAI for biology
- GenAI for power systems
- Multiagent systems safety
If you are interested in working with me, please fill out this interest form. After completing the form, you are welcome to reach out by email. I will read all submissions and emails, though I may not be able to reply to each individually.
Research Sponsors
Our group is grateful for the generous support of our sponsors.
Highlighted Work
Constraint-Aware Generative AI for Science and Engineering
How RAISE Lab makes generative models obey physics, geometry, logic, and safety requirements.
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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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Massively Speeding up LLM inference with Speculative Diffusion Decoding
Faster LLM inference with discrete diffusion drafting and alignment.
Learn moreRecent News
43 total-
Sep 2026
Publication- Six papers from the RAISE Lab accepted at NeurIPS 2026! The work spans constrained generative models, safe code generation, robot planning, and multi-agent safety. Congratulations to our students and collaborators!
- Search-Augmented Masked Diffusion Models for Constrained Generation
- Constrained Code Generation with Discrete Diffusion
- Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
- Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
- Drift Flow Matching
- Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
- New preprint: SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents. We study how agent self-updates can improve task completion while introducing safety failures, even without adversarial intervention.
- I moderated a panel discussion and joined the Genesis Mission project panel at the ORNL AI CORE Workshop at UVA, September 14–15.
- Welcome to Chenrui Ma, Yusuf Güven, and Vu-Anh Le, who have joined the RAISE Lab! -
Aug 2026
Publication- New preprint Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion!
- New preprint From Positionwise Confidence to Prefix Scheduling: Verifier Skipping in Speculative Decoding!
- Our project, From Static to Adaptive Grids: Autonomous Topology Control at Scale, was selected for funding under the U.S. Department of Energy’s Genesis Mission! In collaboration with Los Alamos National Labs, Siemens Energy.
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Jul 2026
Award- I was awarded a Copenhaver Fellowship, an endowed position at UVA Engineering!
- I gave a talk at the 2026 Deep Learning for Science Summer School in Berkeley! -
May 2026
Publication- Our paper Stability-Constrained AC Optimal Power Flow–A Gaussian Process-Based Approach has been accepted to Sustainable Energy, Grids and Networks. Congrats to Vincenzo!
- New preprints:- Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
- Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
- Constrained Code Generation with Discrete Diffusion
- Simple Self-Conditioning Adaptation for Masked Diffusion Models
- I’ll be teaching a course on constrained-aware generative AI at the AI-SCORE summer school in Chicago later this month. -
Mar 2026
Publication- Our paper Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF has been accepted in the Electric Power Systems Research journal. Congratulations to Vincenzo and Mostafa!
- Together with Kyusang Lee and Hong Joo Moon, we received a 3Cavaliers Funding Award on AI-enabled implantable sensors for personalized monitoring after spinal surgery.
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Feb 2026
Publication- New preprint on multi-agent LLM systems and the emergence of collusive behaviors: Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems.
- New preprint on Masked Diffusion Models + Constraints! The idea uses search augmentation 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 was accepted at MLSys 2026! Congrats to Jameson, 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 IEEE Transactions on Smart Grid on decision-focused learning with neural ODEs for proactive grid resilience management.
- Paper accepted at IEEE SaTML 2026 on data minimization.
- Our work Constrained Molecular Generation with Discrete Diffusion received 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.