About Me
I am a First-year M.S. student in Electrical and Computer Engineering at the University of Michigan, Ann Arbor.
My research interest lies in Generative Models, Multi-Modal AI and their real world applications.
📣 I am actively seeking for PhD positions starting in 26 Spring/Fall.
📚 Education
M.S. in Electrical and Computer Engineering
University of Michigan, Ann Arbor (Aug 2024 - May 2026)B.E. in Digital Media Technology
Communication University of China (Sept 2020 - June 2024)
🔬 Research
In my past research, I mainly focus on designing ultra-low bitrate image compression frameworks utilizing generative models (GANs, Diffusion Model, etc). Please refer to my CV and Publications for more details.
Mobility Transformation Lab
University of Michigan, Ann Arbor | Research Intern (Feb 2025 - Ongoing)
State Key Laboratory of Media Convergence and Communication
Communication University of China, Beijing | Research Assistant (Nov 2022 – Dec 2024)
Advisor: Prof. Qi Mao
- Conducted research on scalable image compression and generative models.
- Developed frameworks integrating AI methods for human-machine collaborative vision.
- Contributed to cross-modal compression techniques utilizing generative models like Stable Diffusion.
📝 Publications
- Scalable Face Image Coding via StyleGAN Prior: Towards Compression for Human-Machine Collaborative Vision.
Qi Mao, Chongyu Wang, Meng Wang, Shiqi Wang, Ruijie Chen, Libiao Jin, Siwei Ma.
Published in IEEE Transactions on Image Processing (TIP), 2024. - Stable Diffusion is a Natural Cross-Modal Decoder for Layered AI-Generated Image Compression.
Ruijie Chen, Qi Mao, Zhengxue Cheng.
Accepted by Data Compression Conference (DCC), 2025.
🧑🏻💻 Internship Experience
Algorithm Intern
DeTool Technology Co., Ltd. (July 2023 – Aug 2023)
- Researched, verified, and adopted open-source implementations of solvers for large sparse linear systems using algebraic multigrid methods.
- Designed ablation experiments to optimize computational modules.
