Title: Mathematics in Scientific Machine Learning

Speaker: Rebecca Willett, University of Chicago

Abstract: Artificial intelligence (AI) and machine learning (ML) are poised to

revolutionize the pace and nature of scientific discovery. The widespread adoption

of AI in the sciences has the potential to integrate scientific inquiry with modes of

hypothesis generation, data analysis, experimental design, and simulation,

transforming our capacity to address scientific problems that currently seem

insurmountable. The mathematical foundations of AI and ML are crucial for highquality,

reproducible, AI-enabled scientific research. However, blindly applying AI

and ML poses significant risks, such as the rapid acceleration of the "reproducibility

crisis" in science. In this talk, I will discuss fundamental machine learning

challenges and opportunities that are particularly relevant to scientific discovery,

such as emulators, generative models, and inverse problems. These problems

underscore the importance of incorporating mathematical and physical models as

well as numerical algorithms into ML frameworks, highlighting exciting directions for

future work.

Please note: as a joint colloquium with the Department of Statistics and Data Science this talk is on Friday at 11 in Chambers Hall.

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