Advancing cancer imaging and diagnosis through mathematical modeling, optimization, and analysis

Early detection of cancer is one of the most important factors influencing a patient's prognosis. Esophageal cancer is a key example, where early detection can increase 5-year survival rates by an order of magnitude. Advanced imaging techniques have been developed to improve early detection, but approaches to modeling, optimization and analysis are lagging. Each of these is a critical aspect of designing and implementing new technologies for cancer diagnosis and screening. In this talk, I will discuss my laboratory's work focused on applying advanced mathematical techniques to advance esophageal cancer screening. This includes modeling biomarker abundance using optical signatures, non-linear optimization of imaging hardware, and classification of tissue using high-dimensional data collected through imaging and genetic sequencing. Ultimately, these methods can pave the way to improved disease screening in the context of esophageal cancer, as well as other diseases.

Speaker will be on Zoom. 

Place: Zoom Password:   applied        


2:30 p.m. Friday


In-Person Math, 501/Online