Brayden Schott

I am interested in using computational methods to advance quantitative clinical care, ranging from medical image acquisition to prognostic modeling. I am especially focused on clinical AI safety and integrating physics principles with data-driven modeling.

My past research focused on developing uncertainty quantification methods for deep learning-based medical image analysis. Moving forward, I plan to expand into broader areas of AI safety, particularly as they relate to generative modeling and MRI. Here, I hope to connect data-driven approaches with image acquisition physics principles to improve the control, reliability, and utility of synthetic image generation.

Publications

https://iopscience.iop.org/article/10.1088/1361-6560/add9df/meta

https://jnm.snmjournals.org/content/66/4/565.abstract

https://link.springer.com/chapter/10.1007/978-3-031-73158-7_12

https://link.springer.com/article/10.1007/s00259-024-06767-x

https://iopscience.iop.org/article/10.1088/1361-6560/ad611d/meta