[News!] I have openings for rotation PhD students and a limited number of MS or advanced undergraduate students in my research group for 2019-20. I also have an opening for a postdoc starting summer 2020 or later. If you are a current student at Stanford who would like to work with me, please send me an email including your interests, CV and transcript. If you would like to do a postdoc with me, please send me an email including your interests and CV. For others not currently at Stanford, I apologize if I may not have the bandwidth to respond.

I am also teaching a new course at Stanford this year: BIODS 220 (CS 271, BIOMEDIN 220) Artificial Intelligence in Healthcare, during Winter Quarter 2020. For more information, please see the course website.

I am a new Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. I recently completed a TEAM postdoctoral fellowship in AI and healthcare at Harvard University, where I was hosted by Susan Murphy and John Halamka. My research has been broadly in the areas of computer vision, machine learning, and deep learning, with particular focus on human activity and video understanding, and applications to healthcare.

I received my Ph.D. from Stanford University in 2018, where I was advised by Fei-Fei Li and Arnold Milstein. During my Ph.D., I spent internships at Facebook AI Research in 2016 and Google Cloud AI in 2017. I also co-taught Stanford's CS231N Convolutional Neural Networks course from 2017-2019, with Justin Johnson and Fei-Fei Li.

Research Group

PhD students:
  • Ali Mottaghi (EE)
PhD rotation students:
  • Arjun Desai (EE)
  • Weston Hughes (CS)
MS students:
  • Kamil Ali (CS)
Undergraduate students:
  • Joy Hsu (CS)
  • Julia Gong (CS)
  • Michael Zhang (CS, Harvard University)
  • Xiaotian Cheng (VSR, Tsinghua University)


A Computer Vision System for Deep Learning-Based Detection of Patient Mobilization Activities in the ICU
Serena Yeung*, Francesca Rinaldo*, Jeffrey Jopling, Bingbin Liu, Rishab Mehra, N. Lance Downing, Michelle Guo, Gabriel M. Bianconi, Alexandre Alahi, Julia Lee, Brandi Campbell, Kayla Deru, William Beninati, Li Fei-Fei, Arnold Milstein
Nature Partner Journals (NPJ) Digital Medicine 2019
Temporal Modular Networks for Retrieving Complex Compositional Activities in Video
Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles
ECCV 2018
[pdf] [project page]
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Michelle Guo, Edward Chou, Shuran Song, De-An Huang, Serena Yeung, Li Fei-Fei
ECCV 2018
Dynamic Task Prioritization for Multitask Learning
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, Li Fei-Fei
ECCV 2018
Computer Vision-based Descriptive Analytics of Seniors’ Daily Activities for Long-term Health Monitoring
Jun-Ting Hsieh*, Zelun Luo*, Niranjan Balachandar, Serena Yeung, Guido Pusiol, Jay Luxenberg, Grace Li, Li-Jia Li, N. Lance Downing, Arnold Milstein, Li Fei-Fei
MLHC 2018
3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities
Bingbin Liu*, Michelle Guo*, Edward Chou, Rishab Mehra, Serena Yeung, N. Lance Downing, Francesca Salipur, Jeffrey Jopling, Brandi Campbell, Kayla Deru, William Beninati, Arnold Milstein, Li Fei-Fei
MLHC 2018
Bedside Computer Vision -- Moving Artificial Intelligence from Driver Assistance to Patient Safety
Serena Yeung, N. Lance Downing, Li Fei-Fei, Arnold Milstein
New England Journal of Medicine 2018
Scaling Human-Object Interaction Recognition through Zero-Shot Learning
Liyue Shen, Serena Yeung, Judy Hoffman, Greg Mori, Li Fei-Fei
WACV 2018
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
Amy Jin, Serena Yeung, Jeffrey Jopling, Jonathan Krause, Dan Azagury, Arnold Milstein, Li Fei-Fei
NIPS 2017 Machine Learning for Health Workshop (Best Paper Award)
WACV 2018
[pdf] [project page] [data]
Tackling Over-pruning in Variational Autoencoders
Serena Yeung, Anitha Kannan, Yann Dauphin, Li Fei-Fei
ICML 2017 Workshop on Principled Approaches to Deep Learning
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
Albert Haque, Michelle Guo, Alexandre Alahi, Serena Yeung, Zelun Luo, Alisha Rege, Jeffrey Jopling, Lance Downing, William Beninati, Amit Singh, Terry Platchek, Arnold Milstein, Li Fei-Fei
MLHC 2017
Learning to Learn from Noisy Web Videos
Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei
CVPR 2017
Jointly Learning Energy Expenditures and Activities using Egocentric Multimodal Signals
Katsuyuki Nakamura, Serena Yeung, Alexandre Alahi, Li Fei-Fei
CVPR 2017
[pdf] [project page]
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori, Li Fei-Fei
IJCV 2017
[pdf] [project page] [data]
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei
CVPR 2016
[pdf] [project page] [code]
Towards Viewpoint Invariant 3D Human Pose Estimation
Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, Li Fei-Fei
ECCV 2016
[pdf] [project page]
Vision-Based Hand Hygiene Monitoring in Hospitals
Serena Yeung, Alexandre Alahi, Zelun Luo, Boya Peng, Albert Haque, Amit Singh, Terry Platchek, Arnold Milstein, Li Fei-Fei
NIPS 2015 Machine Learning for Health Care Workshop
VideoSET: Video Summary Evaluation through Text
Serena Yeung, Alireza Fathi, Li Fei-Fei
CVPR 2014 Egocentric Vision Workshop
[pdf] [project page] [data]
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Quoc Le, Will Zou, Serena Yeung, Andrew Ng
CVPR 2011

Stay tuned for an updated research group website to come!