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

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

  • Adjunct Associate Professor, Departments of Genome Sciences, Electrical and Computer Engineering, and Biomedical Informatics and Medical Education

  • Director of the UW Computational Molecular Biology Program

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

  • Senior Data Science Fellow, eScience Institute; Investigator, Kidney Research Institute; and more

Education

  • PhD Electrical Engineering (Specialty: Artificial Intelligence & Machine Learning), Stanford University, Jan 2009

  • MS Electrical Engineering, Stanford University, June 2003

  • BS Electrical Engineering, and Computer Science, Korea Advanced Institute of Science & Technology, Feb 2001

Contact Information

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

Short Bio:

Prof. Su-In Lee is an Associate 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.


Recent Invited Talks: Keynote speech at RECOMB/ISCB Regulatory and Systems Genomics Conference with Dream Challenges (RSGDREAM 2020) (upcoming); Duke University (upcoming); Inaugural seminar for the Pitt-CMU seminar series on Machine Learning in Medicine (upcoming); Stanford University Center for Cancer Systems Biology Seminar; Harvard T.H. Chan School of Public Health; Harvard/MGH Center for Systems Biology; CMU Machine Learning Seminar Series (upcoming); Computational Genomics Summer Institute; MIT Bioinformatics Seminar (May 2019); UCLA Bioinformatics Seminar, Keynote Speech at Northwest Database Society meeting (talk); Keynote Speech at AAAI workshop; Precision Medicine World Conference 2019; and more

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 Neuroscience, Nature Communications, Journal of Machine Learning Research, and many more

ML Conference Organization - Area Chair for Neural Information Processing Systems (NeurIPS'20, NeurIPS'19); Area Chair for ICLR'21; Area Chair for Uncertainty in Artificial Intelligence (UAI'20); Organizer for Machine Learning in Computational Biology (MLCB'20, MLCB'19)


Research Interests:

Her group seeks to develop approaches based on artificial intelligence (AI) and machine learning (ML) for various biomedical applications. Some of her highlighted research include (i) finding therapeutic targets for Alzheimer’s disease (press articles), (ii) treating cancer based on a patient’s own molecular profile (Nature Communications 2018; Selected by F1000), (iii) core ML work on explainable AI (NeurIPS Dec 2017, which got full Oral (top 1%) and was cited 233 times as of June, 2019; and Nature Machine Intelligence, cover article), and (iv) preventing complications during surgery (Nature BME 2018, cover article), (v) predicting kidney diseases (Nature Machine Intelligence, cover article, (vi) enabling pre-hospital predictions for trauma patients, and (vii) improving our understanding of pan-cancer biology, (viii) human genome, and (ix) gene regulatory networks. Lee Lab is collaborating with biomedical researchers in UW School of Medicine, Allen Institutes, Harvard Medical School, and so on.

  • Computational biology & medicine (bioinformatics)- precision medicine, network biology, & genetics

  • Machine learning - explainable AI, interpretable ML, feature selection, & probabilistic graphical models

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