Xiao Fu's Homepage

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Xiao Fu

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

(July 2020) I'm looking for self-motivated Ph.D. students who are interested in

  • deep unsupervised learning

  • social network analytics

  • hyperspectral imaging

  • convex/nonconvex optimization

Please send me your C.V. if you are interested in working with me. Applicants with strong linear algebra, probability, and optimization background will be given priority.

Note: It is the best if you could elaborate in your email which part of my research interests you, and why. I generally do not respond to emails that look just following a template and vaguely claim interested in one of my research areas listed above.

OSU has waived GRE for admission for the next year (2020 and 2021).

I should mention that the Pacific Northwest is a very nice region to live in. Check out some pictures of Oregon and neighboring regions. The Getty Images website has collections of beautiful pictures of places in this region, for example:

News and Updates

  • 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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  • May 2020:(Grant) We have been fortunate to be funded by the joint program of NSF and Intel on Machine Learning for Wireless Communications. This is a collaborative effort between Oregon State Univ. (Xiao Fu), Northwestern (Dongning Gao), and Univ. of Minnesota (Mingyi Hong).

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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: 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!

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  • Nov. 2019: Two papers were submitted

    • 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’’, submitted to IEEE Transactions on Circuits and Systems for Video Technology.

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  • 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%).

  • Aug 2019: (Grant) : Our proposal (with Rebecca) to NSF on factorization tools for ecological system link prediction has been awarded!

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  • Aug 2019: (Grant) : Another proposal to the Army Research Office on community mining has been awarded!

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  • 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: 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 (Grant) : Our proposal to the Army Research Office on multi-aspect intelligence analysis has been awarded!

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  • 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 this new submission: ‘‘Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization’’. This work uses a combination of randomized block coordinate descent and stochastic proximal gradient to decompose large and dense tensors with constraints and regularizations. The complexity saving is quite surprising. The total number of MTTKRPs (which dominates the CPD complexity) needed for the proposed algorithm is very small (see BrasCPD and AdaCPD in the figure).

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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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  • Sep 2018: Our first IEEE TKDE paper has been accepted! The paper ‘‘Efficient and Distributed Generalized Canonical Correlations Analysis for Big Multiview Data’’ comes from a collaborative work with CMU (Prof. Christos Faloutsos and Prof. Tom Mitchell). Now the team members are spread across the U.S. and the world (OSU,UFL,CMU,UVA,UCR,IIS). Congratualations to all! The full paper will be uploaded soon.

  • Sep 2018: We welcome our new group members Ms. Shahana Ibrahim and Mr. Hang Xiao. Wish everybody a wonderful journey ahead!

  • Aug 2018 (Grant) : Our proposal to NSF ECCS on large-scale multiview analysis has been awarded!

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  • Jul 2018: We have just submitted a journal paper to IEEE Transactions on Smart Grid. See the Pre-print here:

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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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  • Feb 2018: Five papers have been accepted to ICASSP 2018, Calgary, Canada, April 2018  —  congratulations to all!

  • Dec. 2017: Here are some newly submitted articles addressing several different topics:

  • Nov. 2017: Two papers were accepted this week.

    • T. Qiu, X. Fu, N. D. Sidiropoulos, and D. Palomar, ‘‘MISO Channel Estimation and Tracking from Received Signal Strength Feedback’’ accepted to IEEE Transactions on Signal Processing

    • K. Huang, X. Fu, and N. D. Sidiropoulos, ‘‘On Convergence of Epanechnikov Mean Shift,’’ to AAAI 2018 (acceptance rate = 25%.)

  • July 2017: We have recently submitted several papers:

    • X. Fu , K. Huang, E.E. Papalexakis, H. Song, P. Talukdar, N. D. Sidiropoulos, C. Faloutsos, and T. Mitchell,‘‘Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data’’ to IEEE Transactions on Knowledge and Data Engineering

    • T. Qiu, X. Fu, N. D. Sidiropoulos, and D. Palomar, ‘‘MISO Channel Estimation and Tracking from Received Signal Strength Feedback’’ to IEEE Transactions on Signal Processing

    • A. S. Zamzam, X. Fu, E. Dall’Anese and N. D. Sidiropoulos, ‘‘Distributed Optimal Power Flow using Feasible Point Pursuit’’ to IEEE CAMSAP 2017.

  • Mar. 2017: I gave a tutorial at ICASSP 2017 together with Prof. Nikos Sidiropouos, Prof. Vagelis Papalexakis (University of California Riverside) and Prof. L. De Lathauwer (KU Leuven). The title is ‘‘ Tensor Decomposition for Signal Processing and Machine Learning ’’ which is based on our IEEE Transactions on Signal Processing overview paper. Check out the slides and the camera-ready paper.

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

  • Oct. 2016: I was recongnized as the ‘‘Outstanding Postdoctoral Scholar’’ by the Postdoctoral Association, University of Minnesota :) Special thanks goes to my mentor Prof. Nikos Sidiropoulos!

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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

  • June, 2016: We have uploaded an overview paper of tensor decomposition to arXiv.org; see ‘‘Tensor decomposition for signal processing and machine learning’’. In this paper, fundamental aspects of tensor are addressed, which include identifiability issues (and insightful simple proofs!), algorithms, and applications from classical signal processing and recent machine learning topics. The goal of this paper is to provide researchers a starting point of doing tensor-related research.

  • June, 2016: Our EUSIPCO2016 papers have been accepted. Here is the one that deals with the famous unit-modulus quadratic program ‘‘Fast unit-modulus least squares with applications in transmit beaforming’’. We proposed an algorithm that uses a three-line code (see Algorithm 1 in the paper) to approximate this famous problem, which works surprisingly well, and saves memory and runtime substantially compared to some popular approaches, e.g., semidefinite relaxation.

  • April, 2016: We have submitted a manuscript titled ‘‘Learning from hidden traits: Joint factor analysis and latent clustering’’ to IEEE Transactions on Signal Processing. Motivated by the fact that many data (e.g., documents and handwritten digits) exhibit better cluster structure in some latent domain relative to the original data domain, we propose a formulation that seeks such cluster-aware dimensionality reduction.

  • April, 2016: The matlab codes of the DANSER algorithm and the RMUSIC algorithm have been uploaded; click DANSER and RMUSIC. A demo of the algorithms in the paper ‘‘Semi-blind hyperspectral unmixing in the presence of spectral libarary mismaches" can be found here.

  • April, 2016: Two papers have been accepted recently:

    • ‘‘Semi-blind hyperspectral unmixing in the presence of spectral libarary mismaches", by IEEE Transactions on Geoscience and Remote Sensing; see the ArXiv version here.

    • ‘‘Power spectra separation via structured matrix factorization’’, by IEEE Transactions on Signal Processing; check out the pre-print here.

  • Mar. 2016: We have submitted a journal paper, titled ‘‘Robust volume minimization-based matrix factorization for remote sensing and document clustering’’, to IEEE Transactions on Signal Processing.

  • Feb. 2016: We have submitted a journal paper ‘‘Fast unit-modulus least squares with applications in beamforming and phase retrieval’’ to IEEE Transactions on Signal Processing. A conference version has also been submitted to EUSIPCO2016.

  • Feb. 2016: Check out the paper ‘‘Robust volume minimization-based structured matrix factorization via alternating optimization’’. We look into an important matrix factorization model in hyperspectral imaging and topic mining, where the data are considered from a convex hull. We find the loading factors via solving a simplex-volume minimization problem. We pay special attention to a practical problem in this structured matrix factorization model, namely, the outlier sensitivity. This paper will be presented in ICASSP2016, Mar. 20-25, 2016, Shanghai, China.

  • Dec. 2015: Our paper ‘‘Robust volume minimization-based structured matrix factorization via alternating optimization’’ has been accepted by IEEE ICASSP 2016, Shanghai, China.

  • Dec. 2015: Our paper ‘‘Joint Factor Analysis and Latent Clustering’’ has been presented at IEEE CAMSAP 2015. Our first author Bo Yang won a Best Student Paper Award at this conference - Congratulations Bo!

  • Dec. 2015: I gave two talks in the Digital Technology Center at University of Minnesota, Minneapolis, MN55455, United States, and in the School of Electronic Engineering at University of Electronic Science and Technology of China, Chengdu, China, respectively; the title of the talks was ‘‘A Structured Matrix Factorization Model for Signal Prcoessing and Machine Learning’’.

  • Sep. 2015: We have just submitted a journal paper titled “Robustness analysis of structured matrix factorization via self-dictionary mixed-norm optimization” to IEEE Signal Processing Letters.

  • Jul. 2015: Check out this paper which has been accepted by IEEE Transactions on Signal ProcessingA factor analysis framework for power spectra separation and multiple emitter localization,” . We consider a scenario in wireless communication, where multiple emitters exist, and the receivers wish to know their locations and their individual power spectra. This problem finds its application in dynamic spectrum access systems, e.g., cognitive radio. It may also be used for intelligent beamforming, routing, and scheduling. Existing spectrum sensing approaches mostly consider estimating the aggregate spectrum of the received signal, rather than the underlying spectral atoms, i.e., individual spectra corresponding to different sources. We consider modeling, formulating, and solving this problem; robustification against sensor failure is also considered.

  • Jul. 2015: The paper “Joint Tensor Factorization and Outlying Slab Suppression With Applications” has been accepted by IEEE Transactions on Signal Processing; see the pre-print here ArXiv. In this work, we consider a realistic scenario where some slabs of a tensor is corrupted. Such a setup is commonly seen in speech separation, Fluorescence data analysis, and social network data mining. A simple low-rank tensor factorization algorithm is proposed to deal with this problem, and interesting interpretable results are observed.