Title/abstract/bio for invited talks

Contact Information

  • Paul G. Allen Center; suinlee AT uw.edu

Prof. Su-In Lee, University of Washington, Seattle

  • Paul G. Allen Professor, Paul G. Allen School of Computer Science & Engineering

  • Adjunct Professor of Genome Sciences (GS), Electrical and Computer Engineering (ECE), and Biomedical Informatics and Medical Education (BIME)

  • Director, Computational Molecular Biology Program

  • Director, AI Technology for Health

  • AI Core Director, NIH Nathan Shock Center of Excellence in Basic Biology of Aging

  • Associate Director, Resuscitation Engineering Science Unit, Department of Emergency Medicine


  • PhD Electrical Engineering, Stanford University, Jan 2009

  • MS Electrical Engineering, Stanford University, June 2003

  • BS Electrical Engineering and Computer Science, KAIST, Feb 2001

    • Thesis: "Biologically inspired neural network approach using feature extraction and top-down selective attention for robust optical character recognition" advised by Prof. Soo-Young Lee


Prof. Su-In Lee is a Paul G. Allen Professor in the Paul G. Allen School of Computer Science & Engineering. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory in Computer Science. Before joining the UW in 2010, Lee was a visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University School of Computer Science. She has received the National Science Foundation CAREER Award and been named an American Cancer Society Research Scholar. She has received numerous generous grants from the National Institutes of Health (NIH), the National Science Foundation (NSF), and the American Cancer Society.

Prof. Lee's research has conceptually and fundamentally advanced how AI can be integrated with biomedicine by addressing novel, forward-looking, and stimulating questions, enabled by AI possibilities. For example, when the primary focus of AI applications in the field of medicine was on accurately predicting a patient’s phenotype (e.g., predicting the response to certain chemotherapy based on the patient’s gene expression profile), Prof. Lee focused on why a certain prediction was made, which can point to the molecular mechanisms underlying patient’s phenotype (e.g., drug sensitivity) or insights into how to clinically mitigate the risk of adverse clinical outcomes. This line of work has led to highly cited seminal publications in the field of foundational AI, clinical medicine, and computational molecular biology. Her research aims to push the boundaries of both foundational AI and biomedicine, to address new questions and make novel discoveries from high-throughput molecular data or patient's medical record data.

Selected Invited Talks:

2022 - Keynote speech at ISMB (MLCSB track), Keynote speech at UW School of Dentistry’s Research Day, Keynote speech at Boston University Graduate Program in Bioinformatics Symposium, Stanford Anesthesiology Research Seminar, Cold Spring Harbor Lab, American Aging Association Annual Meeting (May), The AI Health Podcast, Distinguished lecturer series (Genentech)

2021 - LMRL (Learning Meaningful Representations of Life) 2021, Stanford University Biomedical Informatics Research Colloquia (November), Keynote speech at The KOCSEA Technical Symposium (November), Distinguished Speaker Series, Data Science Institute at Columbia University (March), VIP Speaker Seminar at MD Anderson (June), Columbia University Neuroimmunology Division Seminar (May), Morgan Stanley Quantitative Academic (May), Johns Hopkins Medical School (April), Biology of Aging Speaker Series, UW (April), NIH NHGRI Machine Learning in Genomics (April), American Cancer Society Symposium (March), University of Colorado Anschutz Medical Campus (Jan)

2020 - Fulcrum (Dec), Biogen (Oct), Keynote speech at RECOMB/ISCB Regulatory and Systems Genomics Conference with Dream Challenges (RSGDREAM), Duke University, Inaugural seminar for the Pitt-CMU seminar series on Machine Learning in Medicine, Stanford University Center for Cancer Systems Biology Seminar (Jan), CMU Machine Learning Seminar Series (Jan)

2019 - Harvard T.H. Chan School of Public Health, Harvard/MGH Center for Systems Biology, Computational Genomics Summer Institute, MIT Bioinformatics Seminar (May), UCLA Bioinformatics Seminar, Keynote Speech at Northwest Database Society meeting (talk), Keynote Speech at AAAI workshop, Precision Medicine World Conference 2019, and more

Notable Services:

Associate Editor, Science Advances, American Association for the Advancement of Science (AAAS)

Standing Member, NIH Biodata Management and Analysis (BDMA) study section

Conference Organization:

Area Chair for Neural Information Processing Systems (NeurIPS), ICLR, Uncertainty in Artificial Intelligence (UAI), Intelligence Systems in Molecular Biology (ISMB)

Co-Founder/Co-Chair for Machine Learning in Computational Biology (MLCB)

Recent Grant Review:

NSF BIO, NSF CISE panels, NIH study sections (GCAT/MNG/BDMA/K awards/special emphasize panels), and numerous medical foundations

Recent Journal Paper Review:

Science, Nature Methods, Nature Genetics, Nature Biomedical Engineering, Nature Neuroscience, Nature Communications, Journal of Machine Learning Research, and many more

Grants, Awards & Honors:

  • Named as a standing member of the NIH BDMA study section

  • Madrona Prize (1st place), 2019 UW Allen School Annual Research Meeting (Nov 2019)

    • “CoAI: Cost-aware artificial intelligence in health care” (MD/CSE PhD student, Gabriel Erion)

  • NIH/NIA R01 (PI) on Alzheimer's disease research (Feb 2019)

  • Selected as a speaker for Science in Medicine Lecture (Oct 2018)

  • Cover article, Nature Biomedical Engineering (Oct 2018)

  • Madrona Prize (1st place), 2018 UW Allen School Annual Research Meeting (Nov 2018)

    • “Machine learning approach to identifying therapeutic targets for Alzheimer's disease” (CSE PhD student, Safiye Celik)

  • NIH/NLM R21 (MPI) on anesthesiology research funded by NIH (Sept 2018)

  • NIH/NIGMS R35 (PI) "Opening the Black Box of Machine Learning Models" funded by NIH (2018)

  • NSF/ABI (Advances in Bioinformatics) Innovation Award (PI) (2018)

  • Best Lecture: Interpretable Machine Learning in Precision Medicine, Computational Genomics Winter Institute (Feb 2018)

  • Madrona Prize (first runner-up), 2017 UW Allen School Annual Research Meeting (Nov 2017)

    • “A unified approach to interpreting model predictions” (CSE PhD student, Scott Lundberg)

  • Best Paper Award, NIPS workshop "Interpretable Machine Learning for Complex Systems" (2016)

  • NSF/ABI CAREER Award (PI): Learning the Chromatin Network from ENCODE ChIP-Seq Data (2016)

  • NIH/NIA R21 (PI): Machine Learning Approach to Identify Alzheimer's Disease Therapeutic Targets (2015)

  • Named an American Cancer Society Research Scholar (PI): Big Data Approach to Personalized Therapy for Caner (2015)

  • NSF/ABI Innovation Award (PI): Statistical Methods for Biological Network Estimation (2014)

  • Solid Tumor Translational Research Transformative Research Grant (PI) (2014)

  • eScience/ITHS Seed Grants (MPI) (2014)

  • Finalist. Microsoft Research New Faculty Fellowship (2013)

  • UW's Royalty Research Fund (MPI) (2013)

  • Best Paper Award, Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2012)

  • Before 2009:

    • Stanford Graduate Fellowship, 2001-2004

    • Samsung Lee Kun Hee Fellowship, 2002-2006

    • Ministry of Information and Communication Fellowship, 2001-2002

    • The President of KAIST Award (1st runner-up for academic excellence in the undergraduate program based on GPA), 2001

    • Gold Medal (1st place), Samsung Humantech Paper Competition, 2000 "Biologically Inspired Neural Network Approach using Feature Extraction and Top-Down Selective Attention for Robust Optical Character Recognition"

    • Merit-based full scholarship from KAIST, 1997-2001