Hello, I am Yuan-Tung Chou, a graduate student specializing in Computer-Aided (CAE) Division at the Department of Civil Engineering in National Taiwan University. My passion lies in leveraging machine learning and deep learning techniques to solve real-world challenges. (CV)

Research

My research centers on using graph-based deep learning approches to support advancements in structural engineering, including structural analysis and structural design. For more detailed information on my research progress, please visit Research.

Awards

To further refine my skills in applying machine learning to practical challenges, I have participated in various local and global AI-related hackathons, including the NASA Space Apps Challenges, TSMC x Microsoft Careerhack, and AEC Hackathon. My efforts have earned me several hackathon championships and accolades. For a detailed list of the hackathon awards, please refer to Awards.

My Youtube Channel

I enjoy sharing interesting and cool topics with others. During my free time, I frequently upload videos on various topics, including machine learning with graphs and other ML-related techniques (Youtube).

• Machine Learning with Graphs: A Youtube playlist introducing traditional machine learning methods on graphs and graph neural networks.
• GNN Tutorial with PyTorch: A tutorial on implementing graph neural networks with PyTorch.
• GNN Applications: A Youtube playlist introducing how industries apply GNN in their recommendation systes, drug generation, and so on.

Machine Learning Notes

I love taking notes when I learn new things about machine learning. Followings are the links to my machine learning notes and some of the implementation of computer vision models in PyTorch.

• Machine Learning Notes: ML Notes include basic machine learning concepts and models, CNN backbones, GAN variations, object detection and semantic segmentation methods and more in the future.
• Computer Vision PyTorch Implementation: CV PyTorch implementation includes different CNN backbones, GANs, UNet, Transformer, SETR, neural style transfer, image captioning, and more to be updated