Experience

  1. Machine Learning Scientist

    U.S. Food and Drug Administration

    Responsibilities include:

    • Engineered advanced digital pathology image and segmentation mask generative models utilizing shape-based constraints, with architecture designed for seamless extension to 3D volumetric applications.
    • Pioneered high-fidelity digital pathology image synthesis techniques implementing denoising diffusion probabilistic models, achieving superior texture and clinical feature preservation.
    • Developed robust evaluation metrics for synthetic medical images to assess their utility in downstream machine learning tasks, leveraging large-scale foundation models to ensure reliable performance benchmarking.
  2. Machine Learning Intern

    Canon Medical Research Institute USA, Inc.

    Responsibilities include:

    • Developed a deep learning algorithm for X-ray image noise reduction and signal enhancement
    • ‘Transformed research concept into patent-worthy technology to be deployed in next-generation Alphenix/Evolve Edition’
    • ‘Patent filed with USPTO (Attorney Docket Number: 546053US, 547638US)’

Education

  1. PhD Computer Science

    New York University
    Thesis on Semi-Supervised Machine Learning Techniques for Medical Image Denoising and Segmentation. Supervised by Prof Guido Gerig. Presented papers at 3 IEEE conferences and 1 Springer Edition Conference.
    Read Thesis
  2. MEng Electrical Engineering

    New York University

    Courses included:

    • Machine Learning
    • ‘Image & Video Processing’
    • ‘System Optimization Methods’
  3. BSc Electrical Engineering

    Beijing Jiaotong University
Skills & Hobbies
Technical Skills
Python
Data Science
Pytorch
Hobbies
Diving
Running
Photography
Swimming
Languages
80%
English
100%
Chinese
20%
Spanish