Publications

My broad interests lie in the areas of statistical learning from Big Data, networking, wireless communications, and robust, distributed, as well as sparsity-aware signal processing. See also the list of my publications in Google Scholar, IEEEXplore, or DBLP. An overview of my research can be found here.

Copyright notice. The publications on this website have appeared in journals or conference records published by the IEEE or other organizations. This material is presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by the copyright holders. All persons accessing this information are expected to adhere to the terms and constraints invoked by each case's copyright. In most cases, these works may not be copied or re-posted without the explicit permission of the copyright holder.

Theses

  1. G. Mateos, Sparsity Control for Robustness and Social Data Analysis. PhD thesis, University of Minnesota, May 2012. University of Minnesota's best dissertation award honorable mention

  2. G. Mateos, Distributed Adaptive Estimation and Tracking using Ad Hoc Wireless Sensor Networks. Master's thesis, University of Minnesota, July 2009.

Book chapters

  1. M. Mardani, G. Mateos, G. B. Giannakis, ``Big Data,'' in Cooperative and graph signal processing: Principles and applications, P. M. Djuric and C. Richard, Editors, Elsevier, 2018

  2. G. Mateos, S. Segarra and A. G. Marques, ``Inference of graph topology,'' in Cooperative and graph signal processing: Principles and applications, P. M. Djuric and C. Richard, Editors, Elsevier, 2018

  3. G. Mateos and G. B. Giannakis, ``Robust PCA by controlling sparsity in model residuals,'' in Robust Decomposition in Low Rank and Sparse Matrices and its Applications in Image and Video Processing, T. Bouwmans, E. Zahzah, and N. Aybat, Editors, CRC Press, 2015

  4. B. Baingana, P. Traganitis, G. Mateos and G. B. Giannakis, ``Big Data analytics for social networks,'' in Graph Analysis for Social Media, I. Pitas, Editor, CRC Press, 2015

  5. G. B. Giannakis, Q. Ling, G. Mateos, I. D. Schizas, and H. Zhu, ``Decentralized learning for wireless communications and networking,'' in Splitting Methods in Communication and Imaging, Science and Engineering, R. Glowinski, S. Osher, and W. Yin, Editors, New York, Springer, 2015

Research monograph

  1. G. B. Giannakis, S.-J Kim, G. Mateos and K. Slavakis, ``Signal Processing for Big Data,'' Foundations and Trends in Signal Processing, Yonina Eldar, Editor in Chief, 2017. (In preparation)

Journal papers

  1. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, ``A graph Fourier transform with spread frequency components,'' IEEE Transactions on Signal Processing, vol. 65, October 2017 (submitted).

  2. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, ``Identifying the topology of undirected networks from diffused non-stationary graph signals,'' IEEE Journal on Selected Topics in Signal Processing - Special issue on `Signal and information processing for critical infrastructures,' vol. 11, no. 9, Oct. 2017. (submitted)

  3. R. Shafipour, R. A. Baten, M. K. Hasan, G. Ghoshal, G. Mateos, and M. E. Hoque, ``Closing the knowledge gap in an online learning community: Network-analytic discoveries, simulation and prediction,'' Palgrave Communications, vol. 3, September 2017 (submitted).

  4. A. Shoari and G. Mateos, ``Target localization with distributed binary detectors: Sufficient statistics and minimum-variance unbiased estimation,'' IEEE Transactions on Signal and Information Processing over Networks, vol. 3, August 2017 (submitted).

  5. F. Gama, A. G. Marques, G. Mateos and A. Ribeiro, ``Rethinking sketching as sampling: A graph signal processing approach,'' IEEE Transactions on Signal Processing, vol. 64, July 2017 (submitted).

  6. S. Segarra, A. G. Marques, G. Mateos and A. Ribeiro, ``Network topology inference from spectral templates,'' IEEE Transaction on Signal and Information Processing over Networks - Special issue on `Graph signal processing,', vol. 3, no. 3, pp. 467-483, Aug. 2017.

  7. A. Shoari and G. Mateos, ``On the definition and existence of minimum variance unbiased estimator for target localization,'' IEEE Signal Processing Letters, vol. 23, no. 7, pp. 964-968, July 2016.

  8. S. Segarra, G. Mateos, A. G. Marques and A. Ribeiro, ``Blind identification of graph filters,'' IEEE Transactions on Signal Processing , vol. 65, no. 5, pp. 1146-1159, Jan. 2017.

  9. A. Shoari, G. Mateos, and A. Seyedi, ``Analysis of target localization with ideal binary detectors via likelihood function smoothing,'' IEEE Signal Processing Letters, vol. 23, no. 5, pp. 737-741, May 2016.

  10. M. Mardani, G. Mateos, and G. B. Giannakis, ``Subspace learning and imputation for streaming Big Data matrices and tensors,'' IEEE Transactions on Signal Processing , vol. 63, no. 10, pp. 2663-2677, March 2015.

  11. K. Slavakis, S.-J. Kim, G. Mateos, and G. B. Giannakis, ``Stochastic approximation vis-a-vis online learning for Big Data,'' IEEE Signal Processing Magazine - Lecture Notes Column, vol. 31, no. 6, pp. 124-129, November 2014.

  12. K. Slavakis, G. B. Giannakis, and G. Mateos, ``Modeling and optimization for Big Data analytics,'' IEEE Signal Processing Magazine - Special issue on `Signal processing for Big Data,' vol. 31, no. 5, pp. 18-31, September 2014.

  13. B. Baingana, G. Mateos and G. B. Giannakis, ``Proximal-gradient algorithms for tracking cascades over social networks,'' IEEE Journal of Selected Topics in Signal Processing - Special issue on `Signal processing for social networks,' vol. 8, no. 4, pp. 563-575, August 2014.

  14. J.-A. Bazerque, G. Mateos and G. B. Giannakis, ``Rank regularization and Bayesian inference for tensor completion and extrapolation,'' IEEE Transactions on Signal Processing, vol. 61, no. 22, pp. 5689-5703, November 2013.

  15. G. Mateos and G. B. Giannakis, ``Load curve data cleansing and imputation via sparsity and low rank,'' IEEE Transactions on Smart Grid - Special issue on `Optimization methods and algorithms applied to smart grid,' vol. 4, no. 4, pp. 2347-2355, December 2013.

  16. G. Mateos and K. Rajawat, ``Dynamic network cartography,'' IEEE Signal Processing Magazine - Special issue on `Adaptation and learning over complex networks,' vol. 30, no. 3, pp. 129-143, May 2013. Errata

  17. M. Mardani, G. Mateos and G. B. Giannakis, ``Dynamic anomalography: Tracking network anomalies via sparsity and low rank,'' IEEE Journal of Selected Topics in Signal Processing - Special issue on `Anomalous pattern discovery for spatial, temporal, networked, and high-dimensional signals,' vol. 7, no. 1, pp. 50-66, February 2013.

  18. M. Mardani, G. Mateos, and G. B. Giannakis, ``Decentralized sparsity-regularized rank minimization: Algorithms and applications,'' IEEE Transactions on Signal Processing, vol. 61, no. 21, pp. 5374-5388, November 2013.

  19. M. Mardani, G. Mateos, and G. B. Giannakis, ``Recovery of low-rank plus compressed sparse matrices with application to unveiling traffic anomalies,'' IEEE Transactions on Information Theory, vol. 59, no. 8, pp. 5186-5205, August 2013.

  20. G. Mateos and G. B. Giannakis, ``Robust PCA as bilinear decomposition with outlier-sparsity regularization,'' IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 5176-5190, October 2012.

  21. G. Mateos and G. B. Giannakis, ``Distributed Recursive Least-Squares: Stability and Performance Analysis,'' IEEE Transactions on Signal Processing, vol. 60, no. 7, pp. 3740-3754, July 2012.

  22. G. Mateos and G. B. Giannakis, ``Robust Nonparametric Regression via Sparsity Control with Application to Load Curve Data Cleansing,'' IEEE Transactions on Signal Processing, vol. 60, no. 4, pp. 1571 - 1584, April 2012.

  23. J.-A. Bazerque, G. Mateos and G. B. Giannakis, ``Group-Lasso on Splines for Spectrum Cartography,'' IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 4648 - 4663, October 2011.

  24. G. Mateos, J.-A. Bazerque and G. B. Giannakis, ``Distributed Sparse Linear Regression,'' IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5262 - 5276, October 2010.

  25. G. Mateos, I. D. Schizas and G. B. Giannakis, ``Performance Analysis of the Consensus-Based Distributed LMS Algorithm,'' EURASIP Journal on Advances in Signal Processing.

  26. G. Mateos, I. D. Schizas and G. B. Giannakis, ``Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation,'' IEEE Transactions on Signal Processing, vol. 57, no. 11, pp. 4583 - 4588, November 2009.

  27. I. D. Schizas, G. Mateos and G. B. Giannakis, ``Distributed LMS for Consensus-Based In-Network Adaptive Processing,'' IEEE Transactions on Signal Processing, vol. 57, no. 6, pp. 2365-2381, June 2009.

Conference papers

  1. A. Hashemi, R. Shafipour, H. Vikalo, and G. Mateos, ``Sampling and reconstruction of graph signals via weak submodularity and semidefinite relaxation,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Calgary, Canada, Apr. 15-20, 2018 (submitted).

  2. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, ``Digraph Fourier transform via spectral dispersion minimization,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Calgary, Canada, Apr. 15-20, 2018 (submitted).

  3. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, ``Identifying undirected network structure via semidefinete relaxation,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Calgary, Canada, Apr. 15-20, 2018 (submitted).

  4. R. Magu and G. Mateos, ``United Nations General Assembly vote similarity networks,'' Proc. of Intl. Conf. on Complex Networks and their Applications, Lyon, France, Nov. 29-Dec. 01, 2017 (to appear).

  5. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, ``A digraph Fourier transform with spread frequency components,'' Proc. of IEEE Global Conf. on Signal and Information Processing, Montreal, Canada, Nov. 14-16, 2017.

  6. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, ``Network topology inference from non-stationary graph signals,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, New Orleans, LA, Mar. 5-9, 2017.

  7. S. Segarra, A. G. Marques, G. Mateos and A. Ribeiro, ``Robust network topology inference,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, New Orleans, LA, Mar. 5-9, 2017.

  8. F. Gama, A. G. Marques, G. Mateos and A. Ribeiro, ``Rethinking sketching as sampling: Efficient approximate solution to linear inverse problems,'' Proc. of IEEE Global Conf. on Signal and Information Processing, Washington, DC, Dec. 7-9, 2016.

  9. S. Segarra, A. G. Marques, G. Mateos and A. Ribeiro, ``Network topology identification from imperfect spectral templates,'' Proc. of 50th Asilomar Conf. on Signals, Systems ans Computers, Pacific Grove, CA, Nov. 6-9, 2016.

  10. F. Gama, A. G. Marques, G. Mateos and A. Ribeiro, ``Rethinking sketching as sampling: Linear transforms of graph signals,'' Proc. of 50th Asilomar Conf. on Signals, Systems ans Computers, Pacific Grove, CA, Nov. 6-9, 2016.

  11. A. G. Marques, G. Mateos and Y. Eldar, ``SIGIBE: Solving random bilinear equations via gradient descent with spectral initialization,'' Proc. of European Signal Processing Conference, Budapest, Hungary, Aug. 29- Sep. 2, 2016.

  12. S. Segarra, A. G. Marques, G. Mateos and A. Ribeiro, ``Network topology identification from spectral templates,'' Proc. of Statistical Signal Processing Workshop, Palma de Mallorca, Spain, Jun. 26-29, 2016. Best student paper award

  13. S. Segarra, A. G. Marques, G. Mateos and A. Ribeiro, ``Blind identification of graph filters with multiple sparse inputs,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Shanghai, China, Mar. 20-25, 2016.

  14. S. Segarra, G. Mateos, A. G. Marques and A. Ribeiro, ``Blind identification of graph filters with sparse inputs,'' Proc. of 6th Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico, Dec. 13-16, 2015.

  15. B. Baingana, E. Dall'Anese, G. Mateos and G. B. Giannakis, ``Robust kriged Kalman filtering,'' Proc. of 49th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 8-11, 2015. (Invited)

  16. M. Hassanalieragh, A. Page, T. Soyata, G. Sharma, M. Aktas, G. Mateos, B. Kantarci, and S. Andreescu, ``Health monitoring and management using internet-of-things (IOT) sensing with cloud-based processing: Opportunities and challenges,'' Proc. IEEE Int. Conf. on Services Computing, New York, NY, June 27-30, 2015.

  17. M. Araujo, S. Gunnemann, G. Mateos and C. Faloutsos, ``Beyond blocks: Hyperbolic community detection,'' Proc. European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, September 15-19, 2014.

  18. M. Mardani, G. Mateos and G. B. Giannakis, ``Imputation of streaming low-rank tensor data,'' Proc. of Sensor Array and Multichannel Signal Processing Wkshp., A Coruna, Spain, June 22-25, 2014.

  19. B. Baingana, G. Mateos and G. B. Giannakis, ``A proximal gradient algorithm for tracking cascades over social networks,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Firenze, Italy, May 4-9, 2014.

  20. B. Baingana, G. Mateos and G. B. Giannakis, ``Dynamic structural equation models for tracking topologies of social networks,'' Proc. of 5th Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Saint Martin, Dec. 15-18, 2013.

  21. B. Baingana, G. Mateos and G. B. Giannakis, ``Dynamic structural equation models for tracking cascades over social networks,'' Proc. of NIPS Workshop on Frontiers of Network Analysis, Lake Tahoe, NV, Dec. 9, 2013.

  22. M. Mardani, G. Mateos and G. B. Giannakis, ``Rank minimization for subspace tracking from incomplete data,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Vancouver, Canada, May 26-31, 2013.

  23. J. A. Bazerque, G. Mateos and G. B. Giannakis, ``Inference of Poisson count processes using low-rank tensor data,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Vancouver, Canda, May 26-31, 2013.

  24. G. Mateos and G. B. Giannakis, ``Load Curve Data Cleansing and Imputation via Sparsity and Low Rank,'' Proc. of 3rd Intl. Conf. on Smart Grid Communications, Tainan, Taiwan, Nov. 5-8, 2012. (Invited)

  25. J,-A. Bazerque, G. Mateos and G. B. Giannakis, ``Nonparametric Low-Rank Tensor Imputation,'' Proc. of Statistical Signal Processing Workshop, Ann Arbor, MI, Aug. 5-8, 2012.

  26. M.Mardani, G. Mateos and G. B. Giannakis, ``Exact Recovery of Low-Rank Plus Compressed Sparse Matrices,'' Proc. of Statistical Signal Processing Workshop, Ann Arbor, MI, Aug. 5-8, 2012.

  27. M.Mardani, G. Mateos and G. B. Giannakis, ``Distributed Nuclear Norm Minimization for Matrix Completion,'' Proc. of 13th Intl. Workshop on Signal Process. Advances in Wireless Commun., Cesme, Turkey, Jun. 17-20, 2012. Best student paper award

  28. M.Mardani, G. Mateos and G. B. Giannakis, ``Unveiling Anomalies in Large-Scale Networks via Sparsity and Low Rank,'' Proc. of 45th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 6-9, 2011. (Invited)

  29. G. Mateos and G. B. Giannakis, ``Robust Conjoint Analysis by Controlling Outlier Sparsity,'' Proc. of European Signal Processing Conference, Barcelona, Spain, Aug. 29- Sep. 2, 2011. (Invited)

  30. G. B. Giannakis, G. Mateos, S. Farahmand, V. Kekatos and H. Zhu , ``USPACOR: Universal Sparsity-Controlling Outlier Rejection,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Prague, Czech Republic, May. 22-27, 2011.

  31. G. Mateos and G. B. Giannakis, ``Robust Nonparametric Regression by Controlling Sparsity,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Prague, Czech Republic, May. 22-27, 2011.

  32. J.-A. Bazerque, G. Mateos and G. B. Giannakis, ``Basis Pursuit for Spectrum Cartography,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Prague, Czech Republic, May. 22-27, 2011.

  33. G. Mateos, J.-A. Bazerque and G. B. Giannakis, ``Parallelizable Algorithms for the Selection of Grouped Variables,'' Proc. of 14th DSP Wkshp., Sedona, AZ, Jan. 4-7, 2011. Student paper contest finalist

  34. G. Mateos and G. B. Giannakis, ``Sparsity Control for Robust Principal Component Analysis,'' Proc. of 44th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 7-10, 2010. (Invited)

  35. H. Zhu, G. Mateos, G. B. Giannakis, N. D. Sidiropoulos, and A. Banerjee, ``Sparsity-Cognizant Overlapping Co-Clustering for Behavior Inference in Social Networks,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Dallas, TX, March 14-March 19, 2010.

  36. J.-A. Bazerque, G. Mateos and G. B. Giannakis, ``Distributed Lasso for In-Network Linear Regression,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Dallas, TX, March 14-March 19, 2010.

  37. G. Mateos, J.-A. Bazerque and G. B. Giannakis, ``Spline-based Spectrum Cartography for Cognitive Radios,'' Proc. of 43rd Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 1-4, 2009.

  38. G. Mateos, I. D. Schizas and G. B. Giannakis, ``Closed-form MSE perfomance of the distributed LMS algorithm,'' Proc. of 13th DSP Wkshp., Marco Island, FL, Jan. 4-7, 2009.

  39. I. D. Schizas, G. Mateos and G. B. Giannakis, ``Stability analysis of the consensus-based distributed LMS algorithm,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Las Vegas, NV, March 30-April 4, 2008.

  40. I. D. Schizas, G. Mateos and G. B. Giannakis, ``Consensus-based distributed recursive least-squares estimation in ad hoc wireless sensor networks,'' Proc. of 41st Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 4-7, 2007.

  41. G. Mateos, I. D. Schizas and G. B. Giannakis, ``Consensus-based distributed least-mean square algorithm using wireless ad hoc networks,'' Proc. of 45th Allerton Conf., Univ. of Illinois at U-C, Monticello, IL, Sept. 26-28, 2007.

Patents

  1. G. B. Giannakis, E. Dall'Anese, J. A. Bazerque, H. Zhu, and G. Mateos, ``Robust parametric power spectral density (PSD) map construction." US Patent No. 9,363,679, June 2016.

  2. G. B. Giannakis, J. A. Bazerque, and G. Mateos, ``Non-parametric power spectral density (PSD) map construction." US Patent No. 9,191,831, November 2015.