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.
Learning From Images
This blog post was written by Cash Cherry, Warin Watson, and Rachelle Lang and published with minor edits. The team was advised by Lars Ruthotto. In addition to this post, the team has also given a midterm presentation, filmed a poster blitz video, created a poster and written a manuscript.
This blog post was written by Clara Armstrong, Olivia Kallay, and Srijon Sarkar and published with minor edits. The team was advised by Lucas Onisk. In addition to this post, the team has also given a midterm presentation, filmed a poster blitz video, created a poster and written a manuscript.
This post was written by Callihan Bertley, Claire Gan, Rishi Leburu, and Malia Walewski. The team was advised by Dr. Deepanshu Verma. In addition to this post, we have also created slides for a midterm presentation, a poster blitz video, and a poster.
