معرفی
Yuzhang Shang serves as an Assistant Professor of Computer Science at the University of Central Florida, where he is affiliated with the Artificial Intelligence Initiative. His research bridges theoretical and applied aspects of efficient artificial intelligence systems.
His academic credentials include:
- Ph.D. in Computer Science from Illinois Institute of Technology
- Dual B.S. in Applied Mathematics and Economics from Wuhan University
Dr. Shang's primary research focuses on developing scalable and efficient AI methodologies, with particular expertise in model compression techniques for deep learning architectures. His work addresses critical challenges in deploying resource-intensive models on edge devices through innovations in quantization, binarization, and dataset distillation. This research spans computer vision, natural language processing, and generative models, aiming to reduce computational costs while maintaining model accuracy.
Analysis of his 2023-2024 publications reveals a concentrated research trajectory in model compression, especially for large language models and diffusion architectures. His work demonstrates methodological innovations in contrastive learning for quantization calibration, causal approaches to data-free quantization, and mutual information optimization for dataset distillation, establishing him as an emerging leader in efficient AI.
His notable recognitions include:
- ML and Systems Rising Stars 2025 by MLCommons
- Award of Excellence in Dissertation Research at Illinois Institute of Technology
While no specific student advisement or grant information is publicly documented, his industry internships at Google DeepMind and Cisco Research indicate strong translational research capabilities. His ongoing work likely involves collaborations through UCF's Artificial Intelligence Initiative to advance practical AI deployment.
As an active member of UCF's research ecosystem, he contributes to the university's Artificial Intelligence Initiative, which fosters interdisciplinary collaboration on cutting-edge AI challenges across academic and industry partners.
