I am a Biomedical Engineering undergraduate student at Peking University with a strong passion for Artificial Intelligence, especially in Generative Models and 3D Vision. My goal is to bridge the gap between cutting-edge computer vision technology and its application in biomedical imaging to solve real-world challenges.

• 🔬 Currently, I am a research assistant at the Computational Scientific Imaging Lab at PKU, advised by Prof. He Sun, where my work focuses on AI-driven electron microscopy.
• 🧠 I am driven by the pursuit of knowledge and enjoy building intuitive learning materials to help others understand complex concepts.
• 🔭 I am actively seeking opportunities for research internships and collaborations in related fields.

Here are some of the projects I'm proud of.

• Title: EM Generalist: A physics-driven diffusion foundation model for electron microscopy
• Contribution: Co-first author (3rd position).
• Description: We proposed a novel method combining data-driven and physics-driven approaches using diffusion models to tackle key challenges in electron microscopy, such as denoising, defocus removal, and 3D super-resolution.
• Status: Currently under review at Nature Communications.

• CV Paper Implementations for Learners
• Goal: To bridge the gap between reading a CV paper and understanding its implementation.
• Features:
  • Concise paper summaries.
  • Refined, minimal core code with detailed explanations.
  • Step-by-step running guides.
• Motivation: To demystify the complex official codebases and make state-of-the-art research more accessible to learners.

I believe in the power of clear, first-principle explanations. I have created three websites to share my understanding of fundamental subjects:

• Intuitive University Mathematics: A guide to intuitively understanding core concepts in Multivariable Calculus, Linear Algebra, Probability Theory, and Functional Analysis.
  • 🔗 Link: https://pkujzx.github.io/Math_Foundations/
• Foundations of 3D Computer Vision: A tutorial series covering Camera Models & Calibration, Single View Metrology, Epipolar Geometry, and Structure from Motion (SfM).
  • 🔗 Link: https://pkujzx.github.io/3D_Vision/
• Introduction to Generative Models: A systematic tutorial on Flow Matching and Diffusion Models. This project starts from the most cutting-edge and unified theoretical frameworks to help learners efficiently grasp the core ideas of generative models.
  • 🔗 Link: https://pkujzx.github.io/Flow-Diffusion/

• Languages: Python
• Frameworks: PyTorch
• Fields of Interest: Computer Vision, Generative Models, Deep Learning, Computational Imaging

• Email: jzx417889065@stu.pku.edu.cn

• Feel free to reach out if you are interested in my work or potential collaborations!