Researcher · Builder · Music Producer

Jiahan Li / Ced

Ph.D. Student in Computer Science, NYU Courant
Hi, I am Jiahan Li (李嘉涵), also known as Ced—a name inspired by the CED family of proteins. I am a 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 studied biology in high school and won a gold medal at the Chinese National Biology Olympiad.

My research focuses on generative models for multimodal learning, world modeling, and AI for science, with a particular interest in incorporating scientific structure and prior knowledge.

Outside research, I'm a Hip-Hop producer and DJ. Some prod by me: PKU Cypher 2025, PKU Cypher 2023.

Portrait of Jiahan Li
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Education

  • NYU Courant Institute
    Ph.D. in Computer Science
  • Peking University
    B.S. in Data Science

Experience

  • Google Research
    Student Researcher
  • Tsinghua University
    Research Engineer
  • Helixon Inc. (Earendil Labs)
    Research Engineer
  • Peking University
    Research Intern

Research

Selected Publications

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Figure from 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 AlphaXiv Code Proceedings
Figure from 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
Figure from 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 AlphaXiv Code Proceedings