Xiao Fu's Homepage

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Xiao Fu, Ph.D.

Associate Professor
School of Electrical Engineering and Computer Science
Oregon State University
3003, Kelley Engineering Center
Corvallis, OR, 97331, United States
(xiao.fu(AT)oregonstate.edu)

Links: Google Scholar, OSU AI Program, Signal Processing Group

News and Updates

  • Sep 2025: Our feature article on IEEE Signal Processing Magazine has now been online:

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  • Sep 2025: Sean (Hoang Son Nguyen) has had his first NeurIPS paper accepted!

    • In this work, we study using conditions that were proposed for structured matrix factorization (X=AS) to underpin identifiability of nonlinear mixtures (x=f(s)). It turns out that there is an interesting connection: the sufficiently scattered condition often used for SMF corresponds to diverse influence of latents onto the observed features. This enables unique identification by maximizing the volume of the Jacobian of the mixing function.

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  • August 2025: We are grateful to receive support from NSF:

    • Collaborative Research: Unregistered Spectral Image Fusion: Foundations and Algorithms. This is a collaborative project with University of Minnesota. OSU is the lead institute.

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  • July 2025: Sagar presented our work in ICML 2025 in Vancouver, Canada.

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  • July 2025: Rajesh's first IEEE Transactions on Signal Processing paper is accepted!

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  • Sep. 2024: New papers alert! Subash, Sagar, and Tri have their first NeurIPS papers accepted (pre-prints will be shared soon):

  • Sep. 2024: Honored to receive the Promising Scholar Award from Oregon State University.

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  • Jul. 2024: Check out our new submission on arXiv. This work reviewed the developments of noisy label crowdsourcing from a signal processing perspective, connecting ideas, formulations, and algorithms in this domain to techniques that are widely used in SP, e.g., NMF and tensor decomposition.

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  • Jun. 2024, Dr. Shahana Ibrahim received the 2023/2024 EECS Dissertation Award! Shahana defended her Ph.D. dissertation in summer 2023 and then joined the University of Central Florida as a tenure-track Assistant Professor. Congratulations to Shahana for this well-deserved award!

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  • Jan. 2024: Sagar's first ICLR paper was accepted! In this work we revisited the distribution matching based unsupervised domain translation problem (a typical example is CycleGAN) and offered a model identification perspective. We came up with a provable framework called ‘‘diversified distribution matching’’. This framework provably circumvents the content misalignment problem of CycleGAN. Check out the paper!

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    • Our framework leverages variable-defined subdomains to avoid translating sources to content-misaligned targets.

  • Nov. 2023: Our paper on quantized spectrum cartography has been accepted to IEEE Transactions on Signal Processing! Congratulations to Subash and Sagar!

  • Sep 2023: I am humbled and honored to have received the Engelbrecht Early Career Award from the College of Engineering, Oregon State University. Thank you COE for the recognition. Many thanks to my students, collaborators, mentors and my nominator.

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  • Sep 2023: A new paper was accepted to IEEE CAMSAP 2023. The paper was a result of the REU program under the NSF project on crowdsourced data labeling. The lead author of this paper is our CS undergraduate student Daniel Grey Wolnick. Grey has successfully defended his honors college thesis in the Spring of 2023.

  • Sep 2023: Shahana defended her dissertation in July 2023! In Dec. 2023, Shahana will join the AI Initiative of the University of Central Florida as a Tenure-Track Assistant Professor (joint appointment with the Department of ECE and the Department of CS). Congratulations Shahana! We are proud of you and looking forward to seeing more accomplishments of yours.

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  • July 2023: Group members attended ICML 2023 in Honolulu, Hawaii.

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  • June 2023, I started serving as the Chair of IEEE Signal Processing Society Oregon Chapter. My former chair is Dr. Jinsub Kim.

  • June 2023, I visited KU Leuven, Belgium, hosted by Prof. Aritra Konar and Prof. Lieven De Lathauwer.

  • June 2023, I visited University of Mons, Belgium, hosted by Prof. Nicolas Gillis.

    • Served on the jury (Ph.D. defense committee) of (soon to be Dr.) Pierre De Handschutter.

  • June 2023, I attended ICASSP 2023 in Rhodes, Greece.

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  • Dec. 2022, our papers received two awards:

    • 2022 IEEE Signal Processing Society Donald G. Fink Overview Paper Award: N.D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E.E. Papalexakis, and C. Faloutsos, ‘‘Tensor Decomposition for Signal Processing and Machine Learning’’, IEEE Transactions on Signal Processing, vol. 65, no. 13, pp. 3551-3582, July 1, 2017

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    • The paper shows that post-nonlinear mixture (PNL) learning does not need stringent conditions (e.g., statistical independence of the latent components as often used in nonlinear ICA) for model identifiability. The lesson learned in the paper is that if the latent model is a low-rank matrix, then the PNL is identifiable.

  • June 2022, Qi Lyu has successfully defended his thesis and attended the EECS Graduation Celebration 2022! Congratulations, Dr. Lyu!

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  • May. 2022. Newly accepted paper:

  • May. 2022. Check out our new submission:

  • May. 2022, Our group has received the National Science Foundation Faculty Early Career Development Program Award (NSF CAREER Award). This award will support us to develop exciting nonlinear factor analysis tools for machine learning and signal processing tasks like unsupervised representation learning, self-supervised learning, hyperspectral imaging, and brain signal processing; see the public information from NSF: Click Here

  • Mar. 2022, two of our papers accepted:

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  • Feb. 2022, some newly accepted works

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  • Nov. 2021, two new submissions:

  • Aug. 2021, check out this newly accepted paper:

  • Jul. 2021, Shahana has launched her new website! Click here.

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  • June 2021. Our paper on identifiability of simplex-structured post-nonlinear mixtures has been accepted!

    • Q. Lyu and X. Fu, ‘‘Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder’’, IEEE Transactions on Signal Processing, accepted, June 2021. In this paper we ask a fundamental question: When and how can we identify the matrix factorization model under unknown post-nonlinear distortions; i.e., given y=g(As) with unknown element-wise nonlinear distortion g(.), how to learn A and s in an unsupervised manner? Check out the paper for our most recent take on this problem.

  • June 2021. Check out our new ICML paper:

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  • June 2021. Check out this newly accepted article (to IEEE Transactions on Signal Processing):

    • S. Ibrahim and X. Fu, ‘‘Recovering joint probability of discrete random variables from pairwise marginals’’. This paper answers a fundamental question: Can we recover the joint PMF of N random variables from pairwise marginals? The answer is positive, under some reasonable conditions. We hope this result could shed new light on how to combat the curse of dimensionality in statistical learning.

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  • May 2021. Gave a virtual mini-tutorial (with Nicolas Gillis and Kejun Huang) ‘‘Learning with Nonnegative Matrix Factorization’’ at SIAM Linear Algebra 2021. Slides are here.

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  • April 2021. Check out three pre-prints:

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    • H. Sun, W. Pu, X. Fu, T.-H. Chang, and M. Hong, ‘‘Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective’’, submitted to IEEE Transaction on Signal Processing, April, 2021.

  • Mar. 2021. I am now serving as an Editor of Signal Processing, a publication of the European Association for Signal Processing (EURASIP).

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  • Feb. 2021. Check out the new submission and AAAI paper's arXiv version:

    • E. Seo, R. Hutchinson, X. Fu, C. A. Li, T. Hallman, J. Kilbride, W. Robinson, ‘‘StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling’’, Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021 (acceptance rate = 21%).

    • Y.-C. Miao, X.-L. Zhao, X. Fu, J.-L. Wang, and Y.-B. Zheng, ‘‘Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors’’, submitted to IEEE Transactions on Geoscience and Remote Sensing, Feb. 2021. In this work, we combine the idea of hyper spectral decomposition and unsupervised deep prior to come up with a hyperspectral denoising framework. We leverage the classic linear mixture model to disentangle the spatio-spectral information and impose deep priors on to the both domains. This way, modeling and computational burdens are affordable, even for large scale data like hyperspectral images.

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  • Feb. 2021. Four papers accepted to ICASSP 2021:

    • Shahana got her first two ICASSP papers accepted!

    • Sagar got his first ICASSP paper accepted in his first term at Oregon State University!

    • Congrats to both!

  • Dec. 2020. First good news in December: Eugene Seo's AAAI 2021 paper has been accepted! We design custom neural networks for the species distribution modeling (SDM) problem that is a core task in computational ecology. Several updates related to our recent works:

    • ‘‘StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling’’ AAAI 2021, accepted

    • Q. Lyu and X. Fu, ‘‘Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder’’, submitted to IEEE Transactions on Signal Processing, Nov. 2020.

    • S. Ibrahim and X. Fu, ‘‘Mixed membership graph clustering via systematic edge query’’, submitted to IEEE Transactions on Signal Processing, Dec. 2020

  • Nov. 2020. We are looking for new Ph.D. students!

At least two Ph.D. positions available! Click here for more information. We welcome applicants who are interested in:

  • deep unsupervised learning,

  • social network analysis,

  • statistical machine learning,

  • hyperspectral imaging,

  • tensor/nonnegative matrix factorization,

  • deep learning for wireless communication.

Applicants please send (i) CV, (ii) sample research papers, and (iii) research statement to Dr. Xiao Fu (xiao.fu@oregonstate.edu). Materials received before Jan 1, 2021 will be considered with high priority.

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  • June 2020: Check out this submission ‘‘Hyperspectral super-resolution via interpretable block-term tensor modeling’’. Here we offer an alternative to our previous work on tensor based hyper spectral super-resolution (Kanatsoulis, Fu, Sidiropoulos, and Ma 2018). The new model has two advantages: 1) the recoverability of the super-res. image is guaranteed (as in other tensor models); 2) the latent factors of this model has physical interpretations (but other tensor models do not). The second property allows us to design structural constraints for performance enhancement.

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  • June 2020: Our overview paper on structured tensor and matrix decomposition has been accepted in IEEE Signal Processing Magazine, special issue on ‘‘Non-Convex Optimization for Signal Processing and Machine Learning’’. We discussed a series developments in optimization tools for tensor/matrix decomposition with structural requirements on the latent factors. We introduced inexact BCD, Gauss–Newton (foundation of Tensorlab), and stochastic optimization (with ideas from training deep nets) for tensor and matrix decomposition.

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  • April 2020: another paper accepted!

  • April 2020: two papers accepted!

    • B. Yang, X. Fu, Kejun Huang, N. D. Sidiropoulos, ‘‘Learning Nonlinear Mixture: Identifiability and Algorithm’’ has been accepted by IEEE Transactions on Signal Processing.

  • Mar. 2020: the following paper has been accepted!

  • Mar. 2020: a number of papers accepted!

    • X. Fu, S. Ibrahim, H.-T. Wai, C. Gao, and K. Huang, ‘‘Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization’’, IEEE Transactions on Signal Processing, accepted, Mar 2020. Matlab Code

    • K. Tang, N. Kan, J. Zou, C. Li, X. Fu, M. Hong, H. Xiong ‘‘Multi-user Adaptive Video Delivery over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach’’, IEEE Transactions on Circuits and Systems for Video Technology, accepted, Mar 2020.

    • R. Wu, W.-K. Ma, X. Fu and Q. Li, ‘‘Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation’’, IEEE Transactions on Geoscience and Remote Sensing, accepted, Mar 2020

    • Y. Shen, X. Fu, G. B. Giannakis, and N. D. Sidiropoulos, ‘‘Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices,’’ the IEEE Transactions on Signal and Information Processing over Networks, Special Issue on Network Topology Inference, accepted, Mar. 2020

  • Mar. 2020, Undergraduate Research Assistantship Available: I am looking undergraduate research assistants in EECS at Oregon State University who are interested in statistical machine learning. Please send me your C.V. and transcripts if you are interested in working with me starting summer or Fall 2020 (or Winter 2021). The research experience program will typically be 10 weeks (one term).

  • Mar. 2020, Ph.D. Position available (Research Assistantship): I have always been looking for PhD students who are interested in signal processing and machine learning, especially matrix/tensor factorization models, deep unsupervised learning, and optimization algorithm design. Please send me your C.V. and transcripts (and papers if you have published your work) if you are interested in working with me starting Fall 2020. I would expect some details for why you're interested in my group.

  • Jan. 2020: Two journal papers have been submitted!

    • S. Ibrahim, X. Fu, and X. Li, ‘‘On recoverability of randomly compressed tensors with low CP rank’’, submitted to IEEE Signal Processing Letters, Jan. 2020.

    • X. Fu, N. Vervliet, L. De Lathauwer, K. Huang and N. Gillis, ‘‘Nonconvex optimization tools for large-scale tensor and matrix decomposition with structured factors’’, submitted to IEEE Signal Processing Magazine, Jan. 2020.

  • Dec. 2019: Shahana has her own website online! Please click here to take a look. She has posted the demo of our stochastic tensor decomposition algorithm (BrasCPD and AdaCPD).

  • Dec. 2019: Our paper ‘‘Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks’’ has been accepted by IEEE Transactions on Knowledge and Data Engineering! This paper is co-authored by Xiao, Eugene Seo, Justin Clarke, and Rebecca Hutchinson, all from EECS at Oregon State! Justin was with us as an undergraduate student by the time of submission, and he is now at UMass for his graduate degree. Congratulations, team!

  • Dec. 2019: Shahana presented her first work at NeuriPS 2019, Vancouver, Canada! See the details in the paper ‘‘Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms.’’

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  • Oct. 2019: The paper ‘‘Tensor Completion from Regularly Sampled Data’’ has been accepeted by IEEE Transactions on Signal Processing!

  • Sep 2019: First good news in September! Shahana's first NeuriPS paper ‘‘Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms’’ (S. Ibrahim, X. Fu, N. Kargas, and K. Huang) has been accepted! This year NeuriPS has a record-breaking 6743 submissions, and only 1428 were accepted (= 21%).

  • June 2019: We have submitted a paper (with Ryan, Ken, Qiang) titiled ‘‘Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation’’ to IEEE Transactions on Geoscience and Remote Sensing.

  • June 2019: Our paper (with Cheng and Nikos) ‘‘Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems’’ has been accepeted to IEEE Journal of Selected Topics in Signal Processing!

  • June 2019: We have submitted a paper titled ‘‘Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks’’ to IEEE Transactions on Knowledge and Data Engineering. In this work, we proposed a statistical generative model for ecological network link prediction. The challenge for this type of networks is that all the observed entries suffer from systematic under estimation–which is very different from online recommender systems. This is a collaborative research with Eugene Seo, Justin Clarke, and Rebecca–all from EECS at Oregon State.

  • May 2019: Cheng Gao sucessfully defended his thesis and now is a Master of Science!

  • May 2019: Shahana Ibrahim is awarded a travel grant (sponsored by the National Science Foundation (NSF)) to IEEE Data Science Workshop in Minneapolis, MN, United States. Shahana has a paper ‘‘Stochastic optimization for coupled tensor decomposition with applications in statistical learning’’ accepted to the conference. Congratulations Shahana!

  • April 2019: Our paper (with Kejun) ‘‘Detecting Overlapping and Correlated Communities: Identifiability and Algorithm’’ has been accepted to ICML 2019! This work proposes a new community detection method that has correctness guarantees for identifying the popular mixed membership stochastic blockmodel (MMSB). Many existing methods rely on the existence of ‘‘pure nodes’’ (i.e., nodes in a network that only belong to one community) to identify MMSB. This assumption may be a bit restrictive. Our method leverage convex geometry-based matrix factorization to establish identifiability under much milder conditions.

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  • Jan. 2019, Check out the new paper: ‘‘Learning Nonlinear Mixtures: Identifiability and Algorithm’’. In this work we push forward parameter identifiability of linear mixture models (LMM) to nonlinear ones. LMM finds many applications in blind source separation-related problems, e.g., hyperspectral unmixing and topic mining. In practice, however, the mixing process is hardly linear. This work studies a fundamental question: if there is nonlinearity imposed upon an LMM, can we still identify the underlying parameters of interest? The interesting observation of our work is that: under some conditions, nonlinearity can be effectively removed and the problem will boil down to an LMM identification problem — for which we have tons of tools to handle.

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  • June 2018: I gave a talk in the College of Mathematical Science at University of Electronic Science and Technology of China (UESTC), Chengdu, China. The title is ‘‘Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach’’. See the slides here. The pre-print of the paper is here. Will also be giving this talk at Chongqing University on Jul. 13.

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pictures are from http://www.ieee-icassp2017.org/.

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  • Sep., 2016: Our paper ‘‘Efficient and Distributed Algorithms for Large-Scale Generalized Canonical Correlations Analysis’’ has been accepted by IEEE Internatial Conference on Data Mining (ICDM 2016)! This year ICDM will be held in the week right after NIPS, also in Barcelona. The acceptance rate of ICDM this year is 19.6%.

  • Aug., 2016: Our paper ‘‘Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm’’ has been accepted to the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS). This year NIPS will be held in Decemeber in Barcelona