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Hi, I'm Weiyuan Kong 👋

Computer Vision, Scientific Imaging & Applied AI Engineer

PhD in Biophysics · Image Analysis · Quantitative Microscopy · Scientific Software · AI-Assisted Workflows

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🔬 Overview

I build tools that turn complex scientific images into reliable, measurable data.

My background is in biophysics and quantitative imaging, where I worked on extracting mechanical and morphological information from microscopy experiments. Over time, I became increasingly interested in the engineering side of science: building robust pipelines, automating analysis workflows, validating results, and turning research methods into reusable software.

Today, I work mainly at the intersection of:

  • 🤖 Applied AI & automation
  • 🔬 Scientific imaging
  • 👁️ Computer vision
  • 📊 Quantitative analysis
  • 🛠 Reproducible software engineering

🛠 Tech Stack

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Image Analysis & ML
Engineering & Tools

🚀 Featured Projects

Image dataset quality-control toolkit for computer vision workflows.
Detects duplicates, blur, corruption, exposure issues, and train/validation leakage. Exports HTML, JSON, and CSV reports.
Python OpenCV Data Quality Control Reports

Scientific imaging package for extracting mechanical information from microscopy images.
Includes topology extraction, Bayesian force inference, curvature analysis, and stress summaries.
Python Scientific-Computing Microscopy Bayesian

Android computational photography prototype combining depth estimation with bokeh rendering.
Combines monocular depth estimation with GPU-based bokeh rendering.
Android Kotlin GPU Shaders Computational Photography

Configurable PyTorch U-Net pipeline for biological image segmentation.
Streamlined training, inference, and validation pipelines for microscopy and biological image datasets.
PyTorch U-Net Image Segmentation Deep Learning


📈 GitHub Stats

Weiyuan's GitHub Stats

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💡 Philosophy & Interests

"Good scientific software should not only work once. It should be understandable, testable, reusable, and useful to someone else."

  • Current Focus: Scientific imaging and quantitative microscopy, computer vision for biological/medical images, dataset quality control prior to ML training, inverse problems, and physics-informed image analysis.

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