Seminar: Quantitative Automated Reasoning: The Quest for Scalability
Abstract
The current generation of automated symbolic reasoning techniques excel at qualitative tasks (i.e., when the answer is Yes or No) owing to the dramatic progress in satisfiability solving, also referred to as the SAT revolution. The advances in SAT afford us the luxury to focus on quantitative automated reasoning tasks, whose development is critical
to reason about the increasingly interconnected and complex computing systems that require understanding of probabilistic behavior and distributional properties. In this talk, I will discuss the design of the next generation of automated reasoning techniques to perform higher order tasks such as quantification (aka counting), reasoning about
distributions, and automated synthesis of systems. Naturally, these tasks are hard from a complexity-theoretic viewpoint, and therefore, our frameworks focus on tight integration of real-world applications, beyond the worst-case analysis algorithmic design and data-driven system design. This has allowed us to achieve significant advances in counting, sampling, distribution testing, and synthesis, providing a new algorithmic toolbox in formal methods, probabilistic reasoning, databases, and design verification. I will discuss the core design principles and the utility of the above techniques on various real applications, including quantitative analysis of AI systems and critical infrastructure resilience estimation.
Biography
Dr Kuldeep Meel holds Stephen Fleming Early-Career Associate Professorship in the School of Computer Science, and Associate Professorship at the University of Toronto (on leave). His research interests lie at the intersection of Formal Methods and Artificial Intelligence. He is a recipient of the 2022 ACP Early Career Researcher Award, the 2019
NRF Fellowship for AI, and was named AI's 10 to Watch by IEEE Intelligent Systems in 2020. His research program's recent recognitions include the Distinguished Paper Awards at CAV-23 and CAV-24, ICLP-24 Best Paper Award, 2023 CACM Research Highlight Award, 2022 ACM SIGMOD Research Highlight, IJCAI-22 Early Career Spotlight, Best Paper Award nominations at ICCAD-21 and DATE-23, 1st Place in Model Counting Competition (2020, 2022, and 2024). He is passionate about teaching, and most proud of being recipient of university level Annual Teaching Excellence Awards in 2022 and 2023.