Su-In Lee

Academic Appointments:

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

Education:

  • PhD Electrical Engineering and Computer Science (Artificial Intelligence), Stanford University, 2009
  • MS Electrical Engineering, Stanford University, 2003
  • BS Electrical Engineering and Computer Science, 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 life sciences. 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 233 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.

Lee Lab is collaborating with biomedical researchers in UW School of Medicine, Allen Institutes, Harvard Medical School, University of British Columbia, etc.

  • Computational biology - precision medicine, network biology, & genetics
  • Machine learning - explainable AI, interpretability, 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 and Computer Engineering and Biomedical Informatics and Medical Education at the University of Washington. She is also a Senior Data Science Fellow at the eScience Institute, and Investigator at Kidney Research Institute.

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).

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