Xiaohan Ding is an AI researcher with six years of experience, currently working at ByteDance in Shenzhen after earning a PhD in Computer Science from Tsinghua University. His work spans machine learning, computer vision, and multimodal large language models, with hands-on expertise in refining convolutional architectures such as RepVGG—where he contributed meaningful refactors and optimization to model blocks and training code. He combines deep academic training with production-oriented engineering, delivering reproducible model improvements and efficiency gains. Based in one of China’s leading tech hubs, he bridges cutting-edge research and real-world deployment at scale. Colleagues describe him as someone who pairs rigorous experiment design with pragmatic code-level improvements that accelerate model iteration.
6 years of coding experience
Bachelor of Engineering - BE, Computer Software Engineering, Bachelor of Engineering - BE, Computer Software Engineering at Nanjing University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Tsinghua University
Contributions:122 commits, 1 PR, 155 pushes in 1 year 8 months
Contributions summary:Xiaohan primarily contributed to the `repvgg` repository by updating and refactoring the `repvgg.py` and `train.py` files. These changes included modifications to the RepVGGBlock class, adjustments related to padding and dilation, and updates to the model conversion process. The contributions suggest an active role in refining and optimizing the convolutional neural network architecture for image classification.
Contributions:57 commits, 58 pushes, 36 comments in 2 years 10 months
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