Kun Yu
Instructors

Kun Yu

Instructor

Epidemiology

Other Positions

Assistant Professor of Biomedical Informatics

Biomedical Informatics

Harvard Medical School

Assistant Professor of Pathology

Pathology-Brigham and Women's Hospital

Harvard Medical School


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.

M.D., 06/2011, Medicine
National Taiwan University, Taipei, Taiwan

Ph.D., 09/2016, Biomedical Informatics
Stanford University, Stanford, CA

Ph.D. Minor, 09/2016, Computer Science
Stanford University, Stanford, CA

Google Research Scholar Award2022-2022

National Institutes of Health (NIH) Maximizing Investigators' Research Award2021
National Institutes of Health (NIH)

Blavatnik Center for Computational Biomedicine Award2020-2020

Schlager Family Award for Early Stage Digital Health Innovations2018-2019
Brigham and Women's Hospital

Harvard Data Science Fellowship2017-2019
Harvard University

Pacific Symposium on Biocomputing (PSB) Rigorous Secondary Data Analysis Award2017-2017
Pacific Symposium on Biocomputing (PSB)

Howard Hughes Medical Institute (HHMI) Fellowship2015-2016
Howard Hughes Medical Institute (HHMI)

Winston Chen Stanford Graduate Fellow2012-2016
Stanford University

Best Intern Award, National Taiwan University Hospital2010-2011
National Taiwan University Hospital


Bibliography

Ten quick tips for deep learning in biology.

Lee BD, Gitter A, Greene CS, Raschka S, Maguire F, Titus AJ, Kessler MD, Lee AJ, Chevrette MG, Stewart PA, Britto-Borges T, Cofer EM, Yu KH, Carmona JJ, Fertig EJ, Kalinin AA, Signal B, Lengerich BJ, Triche TJ, Boca SM.

PLoS Comput Biol. 2022 03. 18(3):e1009803. PMID: 35324884