Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m.
Abstract
Neurosymbolic programming combines the otherwise complementary worlds of deep learning and symbolic reasoning, enabling AI solutions that are more accurate, interpretable, and domain-aware. In this talk, Ziyang Li will present Scallop, a programming language and compiler toolchain designed for building neurosymbolic applications. Scallop allows developers to specify a suitable decomposition of learning and reasoning modules. Learning modules integrate seamlessly with modern machine learning frameworks, leveraging everything from custom neural networks to large foundation models for language, vision, and multimodal data. Reasoning modules are specified declaratively, supporting expressive logical patterns, probabilistic inference, and differentiable programming. Li will demonstrate how Scallop simplifies the development of neurosymbolic applications across diverse domains, including image and video analysis, natural language processing, cybersecurity, and bioinformatics. He will conclude with future research directions to advance neurosymbolic programming, addressing the increasing demands of safety-critical, complex, and real-world AI challenges.
Speaker Biography
Ziyang Li is a PhD candidate in computer science at the University of Pennsylvania. His research focuses on neurosymbolic programming, an emerging paradigm that aims to combine the benefits of deep learning and logical reasoning. During his PhD, he developed Scallop, a neurosymbolic programming language and compiler toolchain. Scallop has been used to develop diverse applications in the domains of computer vision, cybersecurity, natural language processing, clinical-decision making, and bioinformatics. Li was awarded an Amazon Web Services Fellowship in 2023 for his research on trustworthy AI and his work has been recognized at leading conferences such as the ACM SIGPLAN Conference on Programming Language Design and Implementation, the Conference and Workshop on Neural Information Processing Systems, the International Conference on Learning Representations, the International Conference on Machine Learning, USENIX Security, and the IEEE Symposium on Security and Privacy. He authored a book on Scallop which was published in the Foundations and Trends in Programming Languages series in 2024.