Su-In Lee

Academic Appointments:

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
  • Senior Data Science Fellow, eScience Institute

Education:

  • PhD Electrical Engineering, Stanford University, 2009
  • MS Electrical Engineering, Stanford University, 2003
  • BS Electrical Engineering, Korea Advanced Institute of Science & Technology, 2001

Contact Information:

  • Office: Rm 536, Paul G. Allen Center (CSE)
  • Email: suinlee AT cs.washington.edu

Talk title/abstract/bio/photo for invited seminars

Research Interests:

Prof. Su-In Lee's lab seeks to develop explainable AI for healthcare and life sciences. Some of her highlighted research include (i) treating cancer based on a patient’s own molecular profile (Nature Communications 2018; Selected by F1000), (ii) preventing complications during surgery (Nature BME 2018; Cover article), (iii) ML theories on explainable AI (NeurIPS Dec 2017; Full Oral; Cited 100+times in 2018), and (iv) finding therapeutic targets for Alzheimer’s disease. More recent projects include (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) protein folding.

Lee Lab is collaborating with biomedical researchers and clinicians in UW School of Medicine (emergency medicine, pathology, nephrology, anesthesiology, hematology, internal medicine, ophthalmology, etc), Allen Institutes, Harvard Medical School, University of British Columbia, etc.

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


Short Bio:

Prof. Su-In Lee is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering, and an Adjunct Associate Professor in the Departments of Genome Sciences, Electrical Engineering and Biomedical Informatics and Medical Education at the University of Washington. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller (Stanford Artificial Intelligence Laboratory). Before joining the UW in 2010, Lee was a visiting professor in the Computational Biology Department at Carnegie Mellon University. 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 (ACS). Lee is currently the PI of the following active grants: NIH/NIA R01, NIH/NLM R21, NIH/NIGMS R35, NSF/BIO INNOVATION, NSF/BIO CAREER, and ACS Research Scholar grant.

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