Description:

Mathematics in Scientific Machine Learning

Rebecca Willett, Professor of Statistics and Computer Science & the Faculty Director of AI at the Data Science Institute, 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 high-quality, 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.

This talk co-sponsored by the Department of Engineering Sciences and Applied Mathematics in McCormick