Computer vision to deeply phenotype human diseases across physiological, tissue and molecular scales.
Date:
09 Sep, 2020 (Wed)
Time:
11:00 am
Webinar Link:
https://hku.zoom.us/j/98522168980?pwd=WkJPaGI5WC81cjdrMWkzMFZNcEYwdz09

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Speaker

Prof. James Zou,
Department of Computer Science Stanford University Stanford CA USA.
Department of Electrical Engineering Stanford University Stanford CA USA.
Department of Biomedical Data Science Stanford University Stanford CA USA.
Chan–Zuckerberg Biohub San Francisco CA USA.

Abstract

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) learning morphodynamics of immune cells, and 4) making genome editing safer (Leenay et al Nature Biotech 2019). Throughout the talk I’ll illustrate the general principles and tools for human-compatible ML that we’ve developed to enable these technologies (Ghorbani et al. ICML 2020, Abid et al. Nature MI 2020).
 
Biography of the speaker:
James Zou is an assistant professor of biomedical data science, CS and EE at Stanford University. He is also a Chan-Zuckerberg investigator and the faculty director of Stanford AI for Health. James develops novel machine learning algorithms that have strong statistical guarantees and that are motivated by human health challenges. Several of his methods are widely used by tech, biotech and pharma companies. He also works on questions important for the broader impacts of AI—fairness, accountability, interpretations, and robustness. He has received several best paper awards at top CS venues, the 2019 RECOMB best paper award, a NSF CAREER Award, a Google Faculty Award, and a Tencent AI award. 
 
Meeting ID: 985 2216 8980
Password: 881353

Organizer

Prof. K.K.M. Tsia

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Tel: (852) 3917 7093
Email: seminar@eee.hku.hk