Our lab is focused on the development and application of artificial intelligence (AI) approaches including machine learning and deep learning for precision oncology. This can lead to the discovery of novel biomarkers for improved treatment response and prognosis prediction, which have the potential to transform cancer care.

We are developing new methods to make these sophisticated AI models more robust, reproducible, and interpretable, all of which are key elements of successful translational applications in medicine. Our work spans across multiple imaging domains and modalities including radiology and pathology images. These data sets are linked with clinical outcomes to address a specific unmet need. Further, we integrate imaging with genomic/molecular data to gain insight into cancer biology. 

Our research is multidisciplinary in nature. We work closely with a team of expert clinicians including oncologists, radiologists, and pathologists at Stanford and beyond. Our goal is to translate new technology and AI-based biomarkers to clinical practice for personalized management and therapy selection, which may ultimately improve outcomes for cancer patients.

Funding

Our lab is supported by multiple NIH R01 grants from the National Cancer Institute (R01CA222512, R01CA233578, R01CA269599, R01CA285456, R01CA290715) and the National Institute of Dental and Craniofacial Research (R01DE030894). Additionally, our work is supported by the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and Sanofi. My earlier research was supported by an NIH Pathway to Independence Award (K99/R00 CA166186) from 2012 to 2017.