Neurosymbolic Program Synthesis (NSP) integrates neural networks and symbolic reasoning to tackle complex tasks requiring both perception and logical reasoning. This talk provides an overview of the NSP framework and its applications in domains such as image editing, data extraction, and robot learning from demonstrations. We will delve into the key ideas behind NSP learning algorithms, focusing on the synergistic interplay between neural guidance and symbolic reasoning. Finally, we will discuss recent advances in ensuring the correctness of synthesized neurosymbolic programs, paving the way for robust and reliable AI systems.

Isil Dillig is a Professor of Computer Science at The University of Texas at Austin, where she leads the UToPiA research group. Her primary research interests span programming languages, formal methods, program synthesis, and software verification. She earned her Bachelor of Science, Master of Science, and Ph.D. degrees in Computer Science from Stanford University. Dr. Dillig’s work has been recognized with honors such as the Sloan Research Fellowship and the NSF CAREER Award, as well as best paper awards at conferences including PLDI, POPL, OOPSLA, and ETAPS. She has served as Program Committee Chair for PLDI 2022 and CAV 2019 and contributed to program committees for many conferences in her field. Finally, her dedication to teaching has been recognized with multiple awards such as the Texas 10 and the College of Natural Sciences Teaching Excellence Award.