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ASSET Seminar: “Formal Methods for Language Model Systems”
January 14 at 12:00 PM - 1:15 PM
- Specify and verify safety properties (e.g., secure code generation, catastrophic risk), yielding stronger guarantees than standard evaluation methods such as benchmarks or red teaming.
- Guide generation with semantic guardrails, ensuring outputs respect formal constraints, substantially improving both reasoning performance and safety.
- Train models that are more performant and safer, and synthesize agents that provably adhere to formally specified constraints (e.g., privacy, resource consumption).
Together, these advances demonstrate that formal methods provide a principled foundation for improving the utility, safety, and efficiency of frontier language model systems.
Gagandeep Singh
Assistant Professor of Computer Science
Gagandeep Singh is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC). He co-leads the Science and Technology working group at the Institute of Government and Public Affairs, University of Illinois. His research combines ideas from formal methods, machine learning, and systems research to develop systematic and theoretically principled approaches for constructing intelligent computing systems with formal guarantees about their behavior and safety.
Singh’s group at UIUC has been at the forefront of advancing trustworthy AI, pioneering state-of-the-art methods for training, verifying, and monitoring language model systems (e.g., LLMs, VLMs, Agentic AI) with formal guarantees. Their work has been recognized through several awards and fellowships, including the NSF Career, Google Research Scholar, multiple Amazon Research Awards, the Qualcomm Innovation Fellowship, and an Open Philanthropy research grant.