Kuldeep Meel

Kuldeep Meel is an Associate Professor in the School of Computer Science at the University of Toronto. His research interests include formal methods, design automation, databases, AI, automated reasoning, synthesis, streaming, probabilistic reasoning, and distribution testing. Meel's work focuses on advancing automated reasoning techniques to address uncertainties in real-world environments, with an emphasis on scalability. He is also affiliated with the Georgia Institute of Technology as a Stephen Fleming Early Career Associate Professor. His research group explores randomized algorithms, statistical inference, and software engineering, with applications in distribution testing and knowledge compilation. Meel has received several awards, including the 2019 NRF Fellowship for AI, the ACP 2022 Early Career Researcher Award, and recognition as AI's 10 to Watch by IEEE Intelligent Systems in 2020.

Kuldeep S. Meel is an Associate Professor at the University of Toronto, specializing in Beyond NP, Automated Reasoning, and Formal Methods. His research focuses on developing scalable algorithms for approximate model counting, probabilistic inference, and SAT solving. He has contributed to the design of efficient CNF-XOR solvers and tools for exact model counting, such as GANAK. His work also explores the intersection of formal methods with machine learning, including quantitative verification of neural networks and reliability estimation for power transmission grids. Meel's research emphasizes the integration of symbolic knowledge into deep networks and the development of parallel scalable techniques for uniform SAT witness generation.