Bio


Dr. Serena Yeung is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. Her research focus is on developing artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. She has extensive expertise in deep learning and computer vision, and has developed computer vision algorithms for analyzing diverse types of visual data ranging from video capture of human behavior, to medical images and cell microscopy images.

Dr. Yeung leads the Medical AI and Computer Vision Lab at Stanford. She is affiliated with the Stanford Artificial Intelligence Laboratory, the Clinical Excellence Research Center, the Center for Artificial Intelligence in Medicine & Imaging, the Center for Human-Centered Artificial Intelligence, and Bio-X. She also serves on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence.

Academic Appointments


Honors & Awards


  • Harvard Technology for Equitable and Accessible Medicine Fellowship, Harvard University (2018 - 2019)

Professional Education


  • Postdoctoral Fellow, Harvard University (2019)
  • Ph.D., Stanford University (2018)

Stanford Advisees


  • Doctoral Dissertation Reader (AC)
    Jingwei Ji, Xuerong Xiao, Darvin Yi
  • Doctoral Dissertation Advisor (AC)
    Ali Mottaghi
  • Master's Program Advisor
    Kamil Ali, Julia Gong, William Locke

All Publications


  • Ethical and Legal Aspects of Ambient Intelligence in Hospitals. JAMA Gerke, S., Yeung, S., Cohen, I. G. 2020

    View details for DOI 10.1001/jama.2019.21699

    View details for PubMedID 31977033

  • A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. NPJ digital medicine Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N. L., Guo, M., Bianconi, G. M., Alahi, A., Lee, J., Campbell, B., Deru, K., Beninati, W., Fei-Fei, L., Milstein, A. 2019; 2: 11

    Abstract

    Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult ICU. Mobility activities were defined as moving the patient into and out of bed, and moving the patient into and out of a chair. A data set of privacy-safe-depth-video images was collected in the Intermountain LDS Hospital ICU, comprising 563 instances of mobility activities and 98,801 total frames of video data from seven wall-mounted depth sensors. In all, 67% of the mobility activity instances were used to train algorithms to detect mobility activity occurrence and duration, and the number of healthcare personnel involved in each activity. The remaining 33% of the mobility instances were used for algorithm evaluation. The algorithm for detecting mobility activities attained a mean specificity of 89.2% and sensitivity of 87.2% over the four activities; the algorithm for quantifying the number of personnel involved attained a mean accuracy of 68.8%.

    View details for DOI 10.1038/s41746-019-0087-z

    View details for PubMedID 31304360

    View details for PubMedCentralID PMC6550251

  • Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos INTERNATIONAL JOURNAL OF COMPUTER VISION Yeung, S., Russakovsky, O., Jin, N., Andriluka, M., Mori, G., Li Fei-Fei 2018; 126 (2-4): 375–89
  • Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos European Conference on Computer Vision Liu, B., Yeung, S., Chou, E., Huang, D., Fei-Fei, L., Niebles, J. 2018: 569–86
  • 3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities Machine Learning in Healthcare Liu, B., Guo, M., Chou, E., Mehra, R., Yeung, S., Downing, N. L., Salipur, F., Jopling, J., Campbell, B., Deru, K., Beninati, W., Milstein, A., Fei-Fei, L. 2018
  • Computer Vision-based Descriptive Analytics of Seniors’ Daily Activities for Long-term Health Monitoring Machine Learning in Healthcare Hsieh, J., Luo, Z., Balachandar, N., Yeung, S., Pusiol, G., Luxenberg, J., Li, G., Li, L., Downing, N. L., Milstein, A., Fei-Fei, L. 2018
  • Dynamic Task Prioritization for Multitask Learning European Conference on Computer Vision Guo, M., Haque, A., Huang, D., Yeung, S., Fei-Fei, L. 2018
  • Neural Graph Matching Networks for Fewshot 3D Action Recognition European Conference on Computer Vision Guo, M., Chou, E., Song, S., Huang, D., Yeung, S., Fei-Fei, L. 2018
  • Bedside Computer Vision - Moving Artificial Intelligence from Driver Assistance to Patient Safety. The New England journal of medicine Yeung, S., Downing, N. L., Fei-Fei, L., Milstein, A. 2018; 378 (14): 1271–73

    View details for PubMedID 29617592

  • Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks Jin, A., Yeung, S., Jopling, J., Krause, J., Azagury, D., Milstein, A., Li Fei-Fei, IEEE IEEE. 2018: 691–99
  • Scaling Human-Object Interaction Recognition through Zero-Shot Learning Shen, L., Yeung, S., Hoffman, J., Mori, G., Li Fei-Fei, IEEE IEEE. 2018: 1568–76
  • Learning to Learn from Noisy Web Videos Yeung, S., Ramanathan, V., Russakovsky, O., Shen, L., Mori, G., Li Fei-Fei, IEEE IEEE. 2017: 7455–63
  • Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance Machine Learning in Healthcare Haque, A., Guo, M., Alahi, A., Yeung, S., Luo, Z., Rege, A., Jopling, J., Downing, N. L., Beninati, W., Singh, A., Platchek, T., Milstein, A., Fei-Fei, L. 2017
  • Jointly Learning Energy Expenditures and Activities using Egocentric Multimodal Signals Nakamura, K., Yeung, S., Alahi, A., Li Fei-Fei, IEEE IEEE. 2017: 6817–26
  • Towards Viewpoint Invariant 3D Human Pose Estimation European Conference on Computer Vision Haque, A., Peng, B., Luo, Z., Alahi, A., Yeung, S., Fei-Fei, L. 2016
  • End-to-end Learning of Action Detection from Frame Glimpses in Video Computer Vision and Pattern Recognition Yeung, S., Russakovsky, O., Mori, G., Fei-Fei, L. 2016: 2678–87

    View details for DOI 10.1109/cvpr.2016.293

  • Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Le, Q. V., Zou, W. Y., Yeung, S. Y., Ng, A. Y. IEEE. 2011