Su-In Lee

Academic Appointments:

  • Associate Professor, Paul G. Allen School of Computer Science & Engineering
  • Adjunct Associate Professor, Dept. of Genome Sciences, Dept. of Electrical Engineering, and Dept. of Biomedical Informatics and Medical Education
  • University of Washington, Seattle

Education:

  • PhD Electrical Engineering, Stanford University, 2009
  • MS Electrical Engineering, Stanford University, 2003
  • BS Electrical Engineering, KAIST, 2001

Contact Information:

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

Research Interests:

  • Computational biology - precision medicine, network biology, & genetics
  • Machine learning - interpretability, feature selection, & 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 Genome Sciences Department, the Department of Electrical Engineering and the Department of Biomedical Informatics and Medical Education at the University of Washington. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory. Before joining the UW in 2010, she 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 a number of generous grants from the National Institutes of Health (NIH), National Science Foundation (NSF), and American Cancer Society (ACS).

Awards & Honors:

  • NIH R01 (PI) (2018)
  • NIH R21 (MPI) on anesthesiology research funded by NIH (2018)
  • NIH R35 (PI) "Opening the Black Box of Machine Learning Models" funded by NIH (2018)
  • NSF/ABI (Advances in Bioinformatics) Innovation Award (PI) (2018)
  • 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
    • Attended the Seoul Science High School, 1995-1997