معرفی
Xiao Fu is an Associate Professor in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds a B.S. (2005) and M.S. (2010) in Communications and Information Engineering and Signal and Information Systems from the University of Electronic Science and Technology of China (UESTC), and a Ph.D. (2014) in Electronic Engineering from The Chinese University of Hong Kong (CUHK). His research focuses on machine learning, signal processing, and optimization, with applications in nonlinear factor analysis, unsupervised learning, and deep neural networks for signal processing tasks. He has received notable awards including the 2022 NSF CAREER Award and the 2024 OSU Promising Scholar Award.
His work emphasizes developing robust algorithms for factor analysis (e.g., tensor and matrix factorization), large-scale optimization in data mining, and deep learning techniques for hyperspectral imaging, radio map estimation, and crowd-sourced label analysis. Key research groups affiliated with him include Data Science and Engineering, Artificial Intelligence and Robotics, and Communications and Signal Processing. His recent projects include advancing unsupervised machine learning to reduce reliance on labeled data in AI systems and exploring applications in environmental sensing and medical imaging.
Xiao Fu's publications span topics like radio map estimation via latent-domain denoisers, noisy label learning with crowd wisdom, and identifiability in nonlinear mixture models. His research bridges theoretical advancements in optimization and practical applications in wireless communication, ecological networks, and biomedical imaging. Current efforts aim to enhance unsupervised deep representation learning and develop scalable algorithms for high-dimensional data analysis.
- Awards: NSF CAREER Award (2022), OSU Promising Scholar Award (2024)
- Grants: NSF-funded CAREER Award project on nonlinear factor analysis tools
- Labs/Teams: Affiliated with interdisciplinary teams in signal processing, AI, and ecological systems modeling
