Our laboratory works with various kinds of data to develop a digital information profile of the patient and the particular type of cancer that needs to be treated. We work on various parts of the problem involving informatics tools, mathematical tools to identify tumor subtypes, image analysis tools to identify variations and radiation resistance, and treatment optimization tools to identify the best course of radiation treatment for that particular patient. We are supported by industrial, federal, and philanthropic sources. Our goal is always to better understand how to improve some part of the cancer research or cancer treatment process. This can be done through the application of advanced mathematical modeling and bioinformatics techniques to understand tumor subtypes better, or it can be done by modeling basic radiobiological factors, integrating patient specific imaging, to better predict and account for disease response two radiation treatments. We often use machine learning or artificial intelligence methods to either analyze genomic factors or as a part of the image based analysis enabling better radiation treatment planning. New approaches to modeling, monitoring, and adapting radiation treatments for cancer provide new hope for patients.