Hello! I am a second-year PhD student in Computer Science at Washington University in St. Louis. I’m fortunate to be advised by Prof. Yixin Chen and to work closely with Prof. Muhan Zhang at Peking University. Previously, I received my master’s degree from Zhejiang University, where I was advised by Prof. Yang Yang, and my bachelor’s degree from Huazhong University of Science and Technology.

My research interests lie primarily in Graph Learning and Large Language Models, and their applications in Relational Database and healthcare.

🔥 News

• 2026.02:  🔥🔥 TabDLM released, we proposed a unified numerical-language diffusion framework for generating high-fidelity tabular data with numerical, categorical and free-form text features.
• 2025.11:  🔥🔥 GRIP released, GRIP is a parameter-efficient framework that injects graph knowledge into LLMs, enabling efficient and context-free reasoning over large-scale text-attributed graphs.
• 2024.12:  🧑‍💻🧑‍💻 Following an agreement with our collaborating hospital and industry partners, we are pleased to make the MBrain code publicly available. The repository can be found here.
• 2024.08:  ✨✨ I started my PhD at Washington University in St. Louis (WashU).

📝 Publications

KDD 2023

MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

Donghong Cai, Junru Chen, Yang Yang, Teng Liu, Yafeng Li

code

MBrain is the first generalized self-supervised learning framework, which can be applied to pre-train both EEG and SEEG signals.

Preprint

TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion

Donghong Cai, Jiarui Feng, Yanbo Wang, Da Zheng, Yixin Chen, Muhan Zhang

code

TabDLM is a unified numerical-language diffusion framework for generating tabular data with both numerical, categorical and free-form text features.

Preprint

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

Jiarui Feng, Donghong Cai, Yixin Chen, Muhan Zhang

GRIP is a parameter-efficient framework that injects graph knowledge into LLMs, enabling efficient and context-free inference over text-attributed graphs.

📖 Educations

• 2024.08 - present, Ph.D., Computer Science, Washington University in St. Louis, Saint Louis, US.
• 2020.09 - 2023.03, Master’s, Software Engineering, Zhejiang University, Hangzhou, China.
• 2016.09 - 2020.06, Bachelor’s, Software Engineering, Huazhong University of Science and Technology, Wuhan, China.

💻 Internships and Industry Experiences

• 2023.04 - 2024.07, Machine Learning Engineer, Alibaba, Shenzhen, China.