A key theme in our research is the use of neurosymbolic programs, i.e., models constructed through the composition of neural networks and traditional symbolic code. The neural modules in such a program facilitate efficient learning, while the symbolic components allow the program to use human domain knowledge and also be human-comprehensible. Our research studies a wide variety of challenges in neurosymbolic programming, including the design of language abstractions that allow neural and symbolic modules to interoperate smoothly, methods for analyzing the safety and performance of neurosymbolic programs, and algorithms for learning the structure and parameters of neurosymbolic programs from data.

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Selected Publications