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 am an incoming Ph.D. student at NYU Courant, advised by Rob Fergus.
Previously, I earned my B.S. degree from Yuanpei College at Peking University in 2023. I also spent three years at an AI-biotech startup, where I worked with Jianzhu Ma and Jian Peng.
I am particularly interested in integrating scientific inductive biases through geometric learning and generative models to tackle new challenges in AI for Science. I also have a broad interest in 3D vision and large language models.
I love music and work as a hip-hop producer/DJ.
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Peking UniversityB.S. in Data ScienceSep. 2019 - Jul. 2023
Experience
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Peking UniversityResearch InternMar. 2021 - Aug. 2021
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Helixon Inc.Research EngineerAug. 2021 - May. 2024 -
Tsinghua UniversityResearch EngineerJune. 2024 - Present
Selected Publications (view all )
Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling
Xiangxin Zhou, Yi Xiao, Mingyu Li, Jiahan Li, Dongyu Xue, Zaixiang Zheng, Jianzhu Ma, Quanquan Gu
International Conference on Machine Learning (ICML) 2025
Designing any type of cyclic peptide vias full-atom diffusion modeling.
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
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.
Categorical Flow Matching on Statistical Manifolds
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.
Full-Atom Peptide Design based on Multi-modal Flow Matching
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.
Proximal Exploration for Model-guided Protein Sequence Design
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.
Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
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.
Equivariant Point Cloud Analysis via Learning Orientations for 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.