Computer Vision to Phenotype Human Diseases Across Physiological and Molecular Scales
James Zou
March 3, 2021, Wednesday, 3:00 PM - 4:00 PM EST
I will present new computer vision algorithms to learn complex morphologies and phenotypes that are important for human diseases. I will illustrate this approach with examples that capture physical scales from macro to micro: 1) video-based AI to assess heart function (Ouyang et al Nature 2020), 2) generating spatial transcriptomics from histology images (He et al Nature BME 2020), 3) and learning morphodynamics of immune cells. Throughout the talk I'll illustrate new design principles/tools for humancompatible and robust AI that we developed to enable these technologies (Ghorbani et al. ICML 2020, Abid et al. Nature MI 2020).
Dr. James Zao is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. He works on making machine learning more reliable, human-compatible and statistically rigorous, and is especially interested in applications in human disease and health. Several of his algorithms are widely used in tech and biotech industries. He received a Ph.D. from Harvard in 2014, and was at one time a member of Microsoft Research, a Gates Scholar at Cambridge and a Simons fellow at U.C. Berkeley. He joined Stanford in 2016 as an inaugural Chan-Zuckerberg Investigator and the faculty director of the university-wide AI for Health program. His lab is also a part of the Stanford AI Lab. His research is supported by the Sloan Fellowship, the NSF CAREER Award, and the Google and Tencent AI awards.