Conference: CBMS Conference: Research at the Interface of Applied Mathematics and Machine Learning

This award supports the NSF-CBMS (Conference Board of Mathematical Sciences) Regional Research Conference in the Mathematical Sciences "Research at the Interface of Applied Mathematics and Machine Learning" to be held December 8-12, 2025, at the University of Houston in Houston, TX. The conference will expose early career researchers to cutting-edge research at the interface of applied mathematics and machine learning.

It will also help identify new research directions and will foster the building of new collaborations between research groups in the Texas-Louisiana area and other regions. The conference will include graduate students, postdoctoral fellows, and established researchers from academia and industry, and provide a platform for early career researchers to learn and discuss recent advances in mathematical methods for machine learning and data science.

In more detail, the conference will feature ten lectures delivered by Dr. Lars Ruthotto from Emory University. The lectures will be divided into three modules.

Module 1 consists of three introduction lectures on machine learning (e.g. deep neural networks, learning problems). The second module, also of three lectures, will introduce important components of applied mathematics in machine learning (e.g. optimization, regularization). The last module will focus on the use of machine learning in critical problems in computational and applied mathematics (e.g. inverse problems, high dimensional partial differential equations).

These lectures will be supplemented by a dozen contributed talks from participants, a poster session, a mentoring academic panel and a second panel that will feature researchers from industry (e.g. oil and gas, medical center). For more information, please visit the conference webpage: http://www.math.uh.edu/cbms-amml.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.