I am a Machine Learning Engineer in ByteDance working in machine auditing for TikTok Shop products. I graduated as a Computer Science Ph.D. from Johns Hopkins University with a background in interpretable computer vision systems for medical image analysis with human-computer interaction, image classification, object detection, and segmentation. I have rich experience with whole slide images, CT scans, and X-rays. I am the first author of Nature partner journal paper. I have excellent communication skills and ability to work on multi-disciplinary teams.
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PhD in Computer Science, 2018-2022
Johns Hopkins University
M.A. in Statistics, 2016-2017
Columbia University
BSc in Physics, 2012-2016
Fudan Univerisity
Interpretable Video Translation by generative AI:
2D-3D style transfer for VR:
Symmetric learning for Fracture Detection in Pelvic Trauma X-ray:
Deep Hierarchical Multi-label Classification of Chest X-ray:
Lung nodule detection in CT images:
The Need for a More Human-Centered Approach to Designing and Validating Transparent AI in Medical Image Analysis - Guidelines and Evidence from a Systematic Review.
An interpretable classification of genetic information for UM prognostication with cell composition analysis.
A two-stage hierarchical multi-label classification algorithm for chest X-ray abnormality classification.