Haomin Chen

Haomin Chen

PhD of Computer Science

Johns Hopkins University

Biography

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.

Download my resumé.

Interests
  • Computer Vision
  • Medical Imaging
  • Transparent System
  • Human-Computer Intraction
  • Generative AI
Education
  • PhD in Computer Science, 2018-2022

    Johns Hopkins University

  • M.A. in Statistics, 2016-2017

    Columbia University

  • BSc in Physics, 2012-2016

    Fudan Univerisity

Skills

Python
PyTorch
TensorFlow
Matlab
C++
R

Experience

 
 
 
 
 
Ericsson
Applied Research Scientist
Ericsson
Feb 2023 – Present Los Angeles, California

Interpretable Video Translation by generative AI:

  • Created the largest dataset of talking head videos from YouTube.
  • Established multi-person & lingual audio/video synchronization.
  • Refined the facial landmark generation network for better articulation.
  • Used diffusion to achieve immersive lip synchronization in videos with translated audio.
 
 
 
 
 
Meta
Research Intern
Meta
Jun 2022 – Aug 2022 Redmond, Washington

2D-3D style transfer for VR:

  • Achieved real-time inference.
  • Enabled human interaction for personalized customization.
  • Preserved 3D visual reality.
  • Utilized PyTorch3D & nvdiffrast as differential rendering.
  • Preserved object style consistency by semantic style transfer.
 
 
 
 
 
PingAn
Applied Research Intern
PingAn
May 2019 – Dec 2019 Bethesda, Maryland

Symmetric learning for Fracture Detection in Pelvic Trauma X-ray:

  • Deployed in Chang Gung Memorial Hospital in Taiwan ans used by > 5000 patients.
  • Paper accepted by ECCV 2020 with poster.
  • Improved AUC from 0.95 to 0.98 and fracture recall from 0.89 to 0.93 (FPR=0.1).
  • Mimicked radiologists to detect fractures by comparing bilateral symmetric regions.
  • Focused on anatomical asymmetry with contrastive learning.
 
 
 
 
 
NVIDIA
Applied Research Intern
NVIDIA
May 2018 – Dec 2018 Bethesda, Maryland

Deep Hierarchical Multi-label Classification of Chest X-ray:

  • Paper accepted by MIDL 2019 with oral presentation.
  • Paper accepted by Journal “Medical Image Analysis”.
  • Mimicked radiologists to classify abnormality with clinical taxonomy.
  • Improved classification AUC from 0.87 to 0.89.
  • Robust to incompletely labeled data and preserved 85% performance drop.
 
 
 
 
 
PingAn
Applied Research Intern
PingAn
May 2017 – Aug 2017 Shanghai

Lung nodule detection in CT images:

  • Achieved rank 6/2887 teams in the Skylake competition by Intel and Alibaba.
  • Applied PyTorch, 3D UNet and Caffe, Faster RCNN in 1000 CT scans.
  • Used fusion method to achieve false positive reduction.

Recent Publications

(2020). Gene Expression Profile Prediction in Uveal Melanoma Using Deep Learning: A Pilot Study for the Development of an Alternative Survival Prediction Tool. In Ophthalmology Retina.

PDF Cite

(2019). Deep hierarchical multi-label classification of chest X-ray images. In MIDL.

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