Hi! I am Chaoran Cheng, a CS Ph.D. candidate at the University of Illinois Urbana-Champaign. I am advised by Prof. Jian Peng. My research interest mainly spans geometric learning for 3D molecules. Specifically, I focus on combining 3D geometries with graph topology to build a comprehensive representation learning model for molecules, proteins, and related downstream tasks. I am also closely working with Prof. Ge Liu on protein-related generative tasks including sequence design and structure optimization.
Out of my pure personal interest, I am developing a machine-learning based automatic charter for Cytoid, a community-driven rhythm game inspired by Rayark’s games Cytus and Cytus II. The community provides many fan-made charts that can be used to train a machine learning system. This project was purely out of personal interest. Cooperation is highly welcomed.
- Equivariant Neural Operator Learning with Graphon ConvolutionIn Thirty-seventh Conference on Neural Information Processing Systems 2023
- Orientation-Aware Graph Neural Networks for Protein Structure Representation LearningArXiv 2022
- Equivariant Point Cloud Analysis via Learning Orientations for Message PassingIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Jun 2022
|Sep 21, 2023
|My work Equivariant Neural Operator Learning with Graphon Convolution was accepted to NeurIPS 2023 as a Spotlight paper. Check the paper here. I am also honored to be the Workflow Chair of the New Frontiers in Graph Learning Workshop at NeurIPS 2023!
|Dec 7, 2022
Check out this post on the Tensor Field Network! ⌬
|Nov 30, 2022
Get a glimpse of Cézanne and the revolution of modern art.