Chaoran Cheng

Hi! I am Chaoran Cheng, a third-year CS Ph.D. candidate at the University of Illinois Urbana-Champaign. I am advised by Prof. Ge Liu, before which I was working with Prof. Jian Peng. My research interest mainly spans multiple AI4Science topics with a special focus on generative tasks on 3D molecules and proteins including sequence-structure co-design and structure optimization. Combining both the continuous and discrete modalities together, our work can build a more comprehensive and versatile generative model for molecules and proteins on diverse downstream tasks.

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.

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

News

May 28, 2024	Check out our latest work of Statistical Flow Matching (SFM) for discrete generation on arXiv! We applied SFM across image, text, and biological domains demonstrate its superior sampling quality and NLL on discrete tasks. (1/4) Can we unleash the power of Riemannian Flow Matching on discrete generation? We introduce “Statistical Flow Matching (SFM)”, a novel FM framework on the manifold of parameterized probability measures inspired by information geometry. Check it out at https://t.co/7aTdwkcNvk pic.twitter.com/QoVkn1mB8I— Chaoran Cheng (@ccr_cheng) May 28, 2024
Sep 21, 2023	My work Equivariant Neural Operator Learning with Graphon Convolution was accepted to NeurIPS 2023 as a Spotlight paper. Check out 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.

Selected Publications

Categorical Flow Matching on Statistical Manifolds

Chaoran Cheng, Jiahan Li, Jian Peng, and Ge Liu

arXiv 2024

Bib HTML

@article{cheng2024categorical,
  title = {Categorical Flow Matching on Statistical Manifolds},
  author = {Cheng, Chaoran and Li, Jiahan and Peng, Jian and Liu, Ge},
  year = {2024},
  journal = {arXiv},
}

Full-Atom Peptide Design based on Multi-modal Flow Matching

Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, and Jianzhu Ma

In International Conference on Machine Learning, ICML 2024

Bib HTML

@inproceedings{li2024fullatom,
  title = {Full-Atom Peptide Design based on Multi-modal Flow Matching},
  booktitle = {International Conference on Machine Learning, {ICML}},
  author = {Li, Jiahan and Cheng, Chaoran and Wu, Zuofan and Guo, Ruihan and Luo, Shitong and Ren, Zhizhou and Peng, Jian and Ma, Jianzhu},
  series = {Proceedings of Machine Learning Research},
  publisher = {{PMLR}},
  year = {2024},
}

Equivariant Neural Operator Learning with Graphon Convolution

Chaoran Cheng, and Jian Peng

In Thirty-seventh Conference on Neural Information Processing Systems 2023

Bib HTML PDF Code

@inproceedings{Cheng2023infgcn,
  title = {Equivariant Neural Operator Learning with Graphon Convolution},
  author = {Cheng, Chaoran and Peng, Jian},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
  year = {2023},
  url = {https://openreview.net/forum?id=EjiA3uWpnc},
}

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

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

Bib HTML PDF Code

@inproceedings{Luo2022CVPR,
  author = {Luo, Shitong and Li, Jiahan and Guan, Jiaqi and Su, Yufeng and Cheng, Chaoran and Peng, Jian and Ma, Jianzhu},
  title = {Equivariant Point Cloud Analysis via Learning Orientations for Message Passing},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2022},
  pages = {18932-18941},
}