Jiahan Li Ced
Peking University
B.S., Peking University (2023)
Hi, my name is Jiahan Li (李嘉涵), but you can also call me Ced. I earned my B.S. degree from Yuanpei College at Peking University in 2023. I also worked as an R&D scientist at an AI biotech startup for three years.
I am particularly interested in integrating scientific inductive biases through geometric learning and generative models to tackle new challenges in AI for Science, with a focus on functional molecule design. I also have a broad interest in other domains, including 3D vision and multimodal language models.
I love music and work as a hip-hop producer.
",
which does not match the baseurl
("
") configured in _config.yml
.
baseurl
in _config.yml
to "
".
Jiahan Li*, Tong Chen*, Shitong Luo, Chaoran Cheng, Jiaqi Guan, Ruihan Guo, Sheng Wang, Ge Liu, Jian Peng, Jianzhu Ma (* equal contribution)
International Conference on Learning Representations(ICLR) 2025
The first multi-stage method combining energy-based models with autoregressive dihedral prediction for peptide binder design.
Chaoran Cheng*, Jiahan Li*, Jian Peng, Ge Liu (* equal contribution)
Conference and Workshop on Neural Information Processing Systems (NeurIPS) 2024
A novel flow matching framework on the manifold of parameterized probability measures for discrete generation.
Jiahan Li*, Chaoran Cheng*, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, Jianzhu Ma (* equal contribution)
International Conference on Machine Learning (ICML) 2024
The first multi-modal multi-task flow matching method for target-specific full-atom peptide design and analysis.
Zhizhou Ren*, Jiahan Li*, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng (* equal contribution)
International Conference on Machine Learning (ICML) 2022 Spotlight
Addressing model-guided sequence design using evolutionary search to find high-fitness mutants with low mutation counts.
Jiahan Li*, Shitong Luo*, Congyue Deng*, Chaoran Cheng*, Jiaqi Guan, Leonidas Guibas, Jian Peng, Jianzhu Ma (* equal contribution)
International Conference on Research in Computational Molecular Biology (RECOMB) 2025
A new type of vector-based neural network combines directional 3D weights with equivariant message passing.
Shitong Luo*, Jiahan Li*, Jiaqi Guan*, Yufeng Su, Chaoran Cheng, Jian Peng, Jianzhu Ma (* equal contribution)
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022 Oral
Learning virtual orientations for each point. Projecting neighbor points to local orientations before aggregating information from them.