On January 11, 2024, Professor of Statistics and Computer Science Rebecca Willett was invited to take part in the NSF Computer and Information Science and Engineering (CISE) Distinguished Lecture series on machine learning and trustworthiness.

Talk Abstract:

Investing in applied machine learning without understanding the underlying foundations is like investing in healthcare without understanding biology. Foundational research has had a tremendous impact on machine learning, from optimization methodology to privacy protections and from data acquisition strategies to uncertainty quantification. Understanding machine learning foundations has also deepened our insights into longstanding challenges in statistics, optimization theory, numerical simulations, and engineering. We have yet to witness the full impact of these foundational developments. In this talk, I will highlight several major examples of these foundations and their impacts on theory, practice, and workforce development. Furthermore, we will explore emerging directions in machine learning for which new theory is necessary and where future foundational research is likely to play a critical role, including scientific discovery and ensuring machine learning methods are fair, safe, equitable, and sustainable.

Watch the video here