My research background is rooted equally in pre-clinical cancer imaging and the computational modeling of tumor growth and response. I now combine these techniques to build personalized mathematical models, creating patient-specific digital twins of their tumors. The overall goal is to use these tumor-specific models and a patient's own data—such as medical imaging—to forecast treatment outcomes.

Computational methods that can accurately predict response to different therapies could dramatically improve patient outcomes and cause a shift in the clinical paradigm. While my efforts originated in the pre-clinical setting, we are now translating this work to the clinic through valued collaborations locally and with the MD Anderson Cancer Center.

This work has been supported by the Joint Center for Computational Oncology (Sponsored by the Oden Institute, Texas Advanced Computing Center, and MD Anderson Cancer Center),  Cancer Prevention Research Institute of Texas (RP220225) and the National Science Foundation (DMS 2436499).

I am currently a Research Scientist within the Center for Computational Oncology
at the Oden Institute in the University of Texas at Austin.

Check out our TumorTwin Python framework for image-based digital twins.
 
Feel free to contact me if you are interested in discussing any of this work, developing new collaborations, or anything else related to computational/mathematical oncology.