[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.
PhD rotation students:
- Arjun Desai (EE)
- Weston Hughes (CS)
- 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
Nature Partner Journals (NPJ) Digital Medicine 2019
Temporal Modular Networks for Retrieving Complex Compositional Activities in Video
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Dynamic Task Prioritization for Multitask Learning
Computer Vision-based Descriptive Analytics of Seniors’ Daily Activities for Long-term Health Monitoring
3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities
Bedside Computer Vision -- Moving Artificial Intelligence from Driver Assistance to Patient Safety
New England Journal of Medicine 2018
Scaling Human-Object Interaction Recognition through Zero-Shot Learning
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
NIPS 2017 Machine Learning for Health Workshop (Best Paper Award)
Tackling Over-pruning in Variational Autoencoders
ICML 2017 Workshop on Principled Approaches to Deep Learning
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
Learning to Learn from Noisy Web Videos
Jointly Learning Energy Expenditures and Activities using Egocentric Multimodal Signals
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Towards Viewpoint Invariant 3D Human Pose Estimation
Vision-Based Hand Hygiene Monitoring in Hospitals
NIPS 2015 Machine Learning for Health Care Workshop
VideoSET: Video Summary Evaluation through Text
CVPR 2014 Egocentric Vision Workshop
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Stay tuned for an updated research group website to come!