Fu's Research Site

My research lies at the intersection of signal processing and artificial intelligence. I use principles from signal processing to make AI models more rigorous, interpretable, and efficient with limited data. I also use modern AI to make sensing, imaging, communications, and optimization systems more adaptive. My work develops theory and algorithms for identifying latent structure in matrix, tensor, and nonlinear mixture models, with particular emphasis on learning from unlabeled data and generative modeling. These methods support applications in domain adaptation and translation, self-supervised and multimodal learning, radio frequency sensing, hyperspectral imaging, data fusion, and wireless communications.

I joined the Department of Electrical and Computer Engineering at the University of Minnesota as an Associate Professor in August 2026. Before that, I was a faculty member in the School of Electrical Engineering and Computer Science at Oregon State University from 2017 to 2026, where I was promoted to Associate Professor in 2023. From 2014 to 2017, I was a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at the University of Minnesota.

Education

Ph.D., Electronic Engineering, 2014, The Chinese University of Hong Kong

Professional Background

Application of principles from signal processing to make AI models more rigorous, interpretable, and efficient with limited data. 

Teaching Subjects

• Probability
• Optimization
• Linear algebra
• Tensor analysis
• Digital signal processing

Honors and Awards

2024 Promising Scholar Award, Oregon State University
2023 Engelbrecht Early Career Award, Oregon State University College of Engineering
2022 IEEE Signal Processing Society Best Paper Award
2022 IEEE Signal Processing Society Donald G. Fink Overview Paper Award
2022 NSF CAREER Award
2016 Outstanding Postdoctoral Scholar, University of Minnesota

Selected Publications

X. Fu, K. Huang, N. D. Sidiropoulos, and W.-K. Ma, "Nonnegative matrix factorization for signal and data analytics: Identifiability, algorithms, and applications." IEEE Signal Processing Magazine, 36(2), 59-80, 2019.

B. Yang, X. Fu, N. D. Sidiropoulos, and M. Hong, "Towards K-means-friendly spaces: Simultaneous deep learning and clustering." International Conference on Machine Learning (ICML), 2017.

M. Ding, X. Fu, T.-Z. Huang, J. Wang, and X.-L. Zhao, "Hyperspectral super-resolution via interpretable block-term tensor modeling." IEEE Journal of Selected Topics in Signal Processing, 15(3), 641-656, 2021.

Q. Lyu, X. Fu, W. Wang, and S. Lu, "Understanding latent correlation-based multiview learning and self-supervision: An identifiability perspective." International Conference on Learning Representations (ICLR), 2022.

S. Ibrahim, T. Nguyen, and X. Fu, "Deep learning from crowdsourced labels: Coupled cross-entropy minimization, identifiability, and regularization. "International Conference on Learning
Representations (ICLR), 2023.

S. Shrestha, X. Fu, and M. Hong, "Deep spectrum cartography: Completing radio map tensors using learned neural models." IEEE Transactions on Signal Processing, 70, 1170-1184, 2022.

S. Shrestha and X. Fu, "Diversified flow matching with translation identifiability." International Conference on Machine Learning (ICML), 2025.

H.-S. Nguyen and X. Fu, "Diverse influence component analysis: A geometric approach to nonlinear mixture identifiability." Advances in Neural Information Processing Systems (NeurIPS), 2025.

Check the complete publication list on Google Scholar