Chaoran Cheng

Department of Computer Science, University of Illinois Urbana-Champaign


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.

I obtained my Bachelor’s Degree in EECS, Peking University, before which I also spent my freshman year studying chemistry.

Selected Publications

  1. Equivariant Neural Operator Learning with Graphon Convolution
    Chaoran Cheng, and Jian Peng
    In Thirty-seventh Conference on Neural Information Processing Systems 2023
  2. Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
    Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jianzhu Ma, and Jian Peng
    ArXiv 2022
  3. Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
    Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, and Jianzhu Ma
    In 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! ‚ƨ
Nov 30, 2022
Get a glimpse of Cézanne and the revolution of modern art.