معرفی
Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks.
Key research areas include:
- Developing robust multimodal models for video entailment and dynamic 3D human reconstruction
- Improving audio-visual segmentation and sound separation through novel adaptation frameworks
- Advancing continual learning methods to handle domain shifts and out-of-distribution data
- Creating submodular optimization strategies for active learning in 3D object detection
His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems.
Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.
