Chen Chen
Ph.D., Associate Professor
My research focuses on multimodal artificial intelligence, computer vision, and efficient learning systems, with an emphasis on large-scale foundation models, human-centric AI, federated and privacy-preserving learning, and high-impact applications in healthcare, public safety, remote sensing, and agriculture.
I am always looking for highly motivated students for research. Please read this Ph.D. opening document for detailed research topics and requirements. How to apply: please visit the "Join Us" page for more information.
Multimodal AI & Foundation Models
Models that jointly reason over images, video, language, and sensor data for robust perception and decision making.
Efficient & Edge-Centric Computer Vision
Resource-aware architectures and training strategies for deployment on embedded, edge devices, and body-worn cameras.
Human-Centric Video Understanding & 3D Reconstruction
Algorithms for human activity recognition, tracking, 2D/3D pose estimation, and mesh reconstruction from monocular and multi-view visual data.
Privacy-Preserving & Federated Learning
Federated learning and privacy-aware training for collaborative model building across institutions without centralizing raw data.
AI for Healthcare, Public Safety, and Agriculture
End-to-end systems that translate core vision research into impact in healthcare, public safety, and agricultural robotics.
ICCV-2025 Conference
MICCAI Conference
ICCV Best Paper Finalists
IEEE/CVF Conference
University of Central Florida
Academy of Science, Engineering and Medicine of Florida (ASEMFL)
World's top 2% scientists
Stanford University/Elsevier
CVPR 2023 Long-form Video Understanding and Generation Workshop
MICCAI Conference
CVPR Best Paper Finalists
IEEE/CVF Conference
UNC-Charlotte Faculty Research Award
University of North Carolina at Charlotte
ACM Multimedia Travel Grant
ACM Multimedia Conference
University of Texas at Dallas
Graduate Research Travel Award
University of Texas at Dallas