Kun-Hsing Yu, M.D., Ph.D.
Title Assistant Professor of Biomedical Informatics Institution Harvard Medical School Department Biomedical Informatics Address Harvard Medical School
Countway Lib DBMI
10 Shattuck St
Boston MA 02115
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Institution
Harvard T.H. Chan School of Public Health
Title
Assistant Professor of Pathology
Institution
Brigham and Women's Hospital
Biography
| Stanford University, Stanford, CA | Ph.D. | 09/2016 | Biomedical Informatics |
| Stanford University, Stanford, CA | Ph.D. Minor | 09/2016 | Computer Science |
| National Taiwan University, Taipei, Taiwan | M.D. | 06/2011 | Medicine |
2022 - 2022
Google Research Scholar Award
2021
National Institutes of Health (NIH) Maximizing Investigators' Research Award
2020 - 2020
Blavatnik Center for Computational Biomedicine Award
2018 - 2019
Schlager Family Award for Early Stage Digital Health Innovations
2017 - 2019
Harvard Data Science Fellowship
2017 - 2017
Pacific Symposium on Biocomputing (PSB) Rigorous Secondary Data Analysis Award
2015 - 2016
Howard Hughes Medical Institute (HHMI) Fellowship
2012 - 2016
Winston Chen Stanford Graduate Fellow
2010 - 2011
Best Intern Award, National Taiwan University Hospital
Overview
Kun-Hsing "Kun" Yu received his PhD in Biomedical Informatics and PhD Minor in Computer Science from Stanford University, and he obtained his MD from National Taiwan University, Taiwan. His research focuses on the integration of quantitative histopathology image patterns with multi-omics (genomics, epigenomics, transcriptomics, and proteomics) profiles to advance cancer research and clinical practice. His team has developed fully-automated algorithms to analyze whole-slide histopathology images at scale, discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells, and identified novel cellular morphologies for patient prognosis. His research interests include quantitative pathology, machine learning, and translational bioinformatics.
Research
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R35GM142879
(YU, KUN-HSING)
Sep 1, 2021 - Jun 30, 2026
Robust, Generalizable, and Fair Machine Learning Models for Biomedicine
Role: Principal Investigator
Bibliographic
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