Jiahan Li Ced

Peking University
Logo B.S., Peking University (2023)

Hi, my name is Jiahan Li (李嘉涵), but you can also call me Ced. I am a first-year CS Ph.D. student at NYU Courant, advised by Rob Fergus and Kyunghyun Cho.

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 insights through generative models to tackle new challenges in AI for Science and Multimodal Large Language Models.

I love music and work as a hip-hop producer/DJ.

Google Scholar
Education
  • Peking University
    Peking University
    B.S. in Data Science
    Sep. 2019 - Jul. 2023
Experience
  • Peking University
    Peking University
    Research Intern
    Mar. 2021 - Aug. 2021
  • Helixon Inc.
    Helixon Inc.
    Research Engineer
    Aug. 2021 - May. 2024
  • Tsinghua University
    Tsinghua University
    Research Engineer
    June. 2024 - Present
Selected Publications (view all )
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
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.

[paper]

Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
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.

[paper]

Categorical Flow Matching on Statistical Manifolds
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.

[paper] [Code] [Proceedings]

Categorical Flow Matching on Statistical Manifolds
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.

[paper] [Code] [Proceedings]

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

[paper] [Code] [Proceedings]

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

[paper] [Code] [Proceedings]

Proximal Exploration for Model-guided Protein Sequence Design
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.

[Paper] [Code] [Proceedings]

Proximal Exploration for Model-guided Protein Sequence Design
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.

[Paper] [Code] [Proceedings]

Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
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.

[Paper]

Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
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.

[Paper]

Equivariant Point Cloud Analysis via Learning Orientations for 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.

[Paper] [Code] [Proceedings]

Equivariant Point Cloud Analysis via Learning Orientations for 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.

[Paper] [Code] [Proceedings]

All publications