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  1. irenechen.netIrene Y. Chen - UC Berkeley and UCSF

    I am an Assistant Professor at UC Berkeley and UCSF in Computational Precision Health and EECS as well as a faculty member of Berkeley AI Research ( BAIR ). I am interested in how we can make machine learning systems for healthcare to be more robust, impactful, and equitable.

  2. scholar.google.com › citations‪Irene Y. Chen‬ - ‪Google Scholar‬

    Articles 1–20. ‪Assistant Professor, UC Berkeley and UC San Francisco‬ - ‪‪Cited by 2,815‬‬ - ‪machine learning‬ - ‪healthcare‬ - ‪equity‬ - ‪precision health‬.

  3. chenlab.ioC.H.E.N. Lab

    Led by Prof. Irene Chen, the Computational Healthcare for Equity and iNclusion (CHEN) Lab is an interdiscplinary research group in Computational Precision Health, EECS, and Berkeley AI Research.

  4. www.chemistry.ucla.edu › directory › chen-ireneChen, Irene A. – UCLA - University of California, Los Angeles

    Irene A. Chen is an associate professor in the Department of Chemical and Biomolecular Engineering at the University of California, Los Angeles. Previously, she was an assistant and then associate professor at the University of California, Santa Barbara.

  5. irenechen.net › about-meBio | Irene Y. Chen - UC Berkeley and UCSF

    Bio: Irene Chen is an assistant professor at UC Berkeley and UCSF in Computational Precision Health and EECS. She studies machine learning systems for healthcare to be more robust, impactful, and equitable.

  6. scholar.google.com › citations‪Irene A. Chen‬ - ‪Google Scholar‬

    Cited by. Year. Dissecting temporal and spatial control of cytokinesis with a myosin II Inhibitor. AF Straight, A Cheung, J Limouze, I Chen, NJ Westwood, JR Sellers, ... Science 299 (5613),...

  7. www2.eecs.berkeley.edu › Faculty › HomepagesIrene Chen | EECS at UC Berkeley

    Irene Chen is an assistant professor at UC Berkeley and UCSF’s Computational Precision Health program with a joint appointment in UC Berkeley EECS. Her work centers on machine learning methods for improving clinical care and making it more equitable, as well as auditing and addressing bias in algorithmic models.