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

  • Associate Professor (with tenure), Paul G. Allen School of Computer Science & Engineering
  • Adjunct Associate Professor, Depts of Genome Sciences, Electrical and Computer Engineering, and Biomedical Informatics and Medical Education
  • Deputy Director of the Computational Molecular Biology Program; Senior Data Science Fellow, eScience Institute; Investigator, Kidney Research Institute; and more


  • PhD Electrical Engineering, and Computer Science (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:

  • Rm 536, Paul G. Allen Center; suinlee AT

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: Harvard T.H. Chan School of Public Health (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, numerous 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'19); Senior Program Committee for Uncertainty in Artificial Intelligence (UAI'20); Organizer for Machine Learning in Computational Biology (MLCB'19)

Research Interests:

Her group seeks to develop approaches based on explainable 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) ML theories on explainable AI (NeurIPS Dec 2017 ; Full Oral (top 1%); Cited 240 times as of June, 2019), and (iv) preventing complications during surgery (Nature BME 2018; Cover article), (v) predicting kidney diseases, (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, University of British Columbia, etc.

  • Computational biology & medicine - precision medicine, network biology, & genetics
  • Machine learning - explainable AI, interpretable ML, feature selection, & probabilistic graphical models

Grants, Awards & Honors:

  • 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 from UW CSE's Industry Affiliates Meeting (2018 and 2017)
  • 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)
  • Publication recommended in F1000Prime as being of special significance in its field - Lee and Celik et al. Nature Communications (2018)
  • Neural Information Processing Systems (NeurIPS) Oral Presentation (Dec 2017)
  • Best Paper Award, NIPS workshop "Interpretable Machine Learning for Complex Systems" (2016)
  • Publication recommended in F1000Prime as being of special significance in its field - Lundberg et al. Genome Biology (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
    • Attended Seoul Science High School, 1995-1997