Call for Summer 2024 Applications

We are pleased to announce that our applications website for REU applicants is now live. This year, we are using NSF’s new ETAP system for the first time, which should make it easier for applicants to apply. We will start reviewing applications on February 1 and all applications received by March 1 will receive full consideration.

For more information about the application process, see this page and stay tuned for a list of projects to be posted a the Summer 2024 tab.

Learning from Images

The amount of imaging data generated every day exceeds human imagination. With their ability to statistically analyze such large datasets, computational algorithms can enhance our ability to discover new patterns and improve imaging data quality for critical healthcare applications and beyond.

This theme’s projects provide new mathematical insights and algorithms enabling learning from image data. The goals of the individual projects include improving algorithms for learning the distribution of image data, optimizing the measurement design to improve image quality, developing efficient algorithms for reconstructing image sequences, and generalizing machine learning techniques to learn transformations between images.

The projects build upon and advance state-of-the-art techniques from machine learning, numerical linear algebra, and differential equations. Students will learn about these techniques and be trained to combine them in new ways to build effective algorithms. Their analysis and experiments will provide new insights into the strengths and weaknesses of their approaches.

Lars Ruthotto
Lars Ruthotto
Winship Distinguished Research Associate Professor

I am interested in developing efficient training algorithms for deep neural networks and their applications and data science and scientific computing (e.g., high-dimensional optimal control and PDEs).