Gonzalo Mateos

Publications

My broad interests lie in the areas of (statistical) graph signal processing for the study of networks, neuroimaging data analysis, and robust, distributed, as well as sparsity-aware signal processing. See also the list of my publications in Google Scholar, ORCID, IEEEXplore, or DBLP. A (somewhat outdated) overview of my research can be found here.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

2. 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

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

Book chapters

5. 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

4. 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

2. 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

1. 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

Journal papers

54. S. Rey, H. Ajorlou, and G. Mateos, ``Directed acyclic graph convolutional networks,'' IEEE Transactions on Signal Processing, vol. 72, November 2024. (working draft)

53. S. Sihag, G. Mateos, and A. Ribeiro, ``Disentangling neurodegeneration with pre-trained covariance neural networks,'' IEEE Signal Processing Magazine - Special issue on `Accelerating Brain Discovery Through Data Science and Neurotechnology', vol. 43, no. 1, January 2026. (whitepaper submitted)

52. S. Sihag, G. Mateos, and A. Ribeiro, ``CoVariance neural networks: Principal component analysis meets learning with graphs,'' IEEE Signal Processing Magazine - Special issue on `The Mathematics of Deep Learning', vol. 42, no. 6, December 2025. (whitepaper submitted)

51. S. Sihag, G. Mateos, and A. Ribeiro, ``NeuroVNN: A pre-trained model for characterizing neurodegeneration using covariance neural networks,'' IEEE Transactions on Medical Imaging, vol. 42, October 2024. (submitted)

50. C. Ye and G. Mateos, ``Network source localization: Robustness to graph perturbations,'' IEEE Signal Processing Letters, vol. 31, September 2024. (working draft)

49. S. Perez, M. Fiori, F. Larroca, P. Bermolen, and G. Mateos, ``LASE: Learned adjacency spectral embeddings,'' Transactions on Machine Learning Research, September 2024. (working draft)

48. B. Marenco, P. Bermolen, F. Larroca, M. Fiori, and G. Mateos, ``Weighted random dot product graphs,'' Electronic Journal of Statistics, vol. 18, no. 2, September 2024. (working draft)

47. C. Ye and G. Mateos, ``Blind Deconvolution on graphs: Exact and stable recovery,'' Signal Processing, vol. 210, September 2024 (revised).

46. M. Wasserman and G. Mateos, ``Graph structure learning with interpretable Bayesian neural networks,'' Transactions on Machine Learning Research, September 2024.

45. M. Fiori, B. Marenco, F. Larroca, P. Bermolen, and G. Mateos, ``Gradient-based spectral embeddings of random dot product graphs,'' IEEE Transactions on Signal and Information Processing over Networks, vol. 10, pp. 1-16, January 2024.

44. O. D. Kose, G. Mateos, and Y. Shen, ``Fairness-aware optimal graph filter design,'' IEEE Journal of Selected Topics in Signal Processing - Special series on `AI in signal and data science,' vol. 18, no. 2, pp. 142-154, March 2024.

43. M. Wasserman, S. Sihag, G. Mateos, and A. Ribeiro, ``Learning graph structure from convolutional mixtures,'' Transactions on Machine Learning Research, May 2023.

42. C. Ye and G. Mateos, ``SLoG-Net: Algorithm unrolling for source localization on graphs,'' IEEE Transactions on Signal and Information Processing over Networks, vol. 10, May 2024 (working draft).

41. S. Sihag, G. Mateos, C. McMillan, and A. Ribeiro, ``Transferability of covariance neural networks,'' IEEE Journal of Selected Topics in Signal Processing - Special series on `AI in signal and data science,' vol. 18, no. 2, pp. 199-215, March 2024.

40. Z. Xiao, H. Fang, S. Tomasin, G. Mateos, and X. Wang, ``Joint sampling and reconstruction of time-varying signals over directed graphs,'' IEEE Transactions on Signal Processing, vol. 71, pp. 2204-2219, May 2023.

39. C. Ye, S. S. Saboksayr, W. Shaw, R. O. Coats, S. L. Astill, G. Mateos, and I. Delis ``A tensor decomposition reveals ageing-induced differences in muscle and grip-load force couplings during object lifting,'' Scientific Reports, vol. 14, pp. 1-13, June 2024.

38. B. Marenco, P. Bermolen, M. Fiori, F. Larroca, and G. Mateos, ``Online change-point detection for weighted and directed random dot product graphs,'' IEEE Transactions on Signal and Information Processing over Networks, vol. 8, pp. 144-159, Feb. 2022.

37. S. S. Saboksayr and G. Mateos, ``Accelerated graph learning from smooth signals,'' IEEE Signal Processing Letters, vol. 28, pp. 2192-2196, Oct. 2021.

36. Y. Li, G. Mateos and Z. Zhang, ``Learning to model the relationship between brain structural and functional connectomes,'' IEEE Transactions on Signal and Information Processing over Networks, vol. 8, pp. 830-843, Oct. 2022.

35. S. S. Saboksayr, G. Mateos, and M. Cetin, ``Online discriminative graph learning from multi-class smooth signals,'' Signal Processing - Special issue on `Processing and learning over graphs', vol. 186, pp. 1-14, Apr. 2021.

34. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, ``Learning directed graphs via graph filter identification,'' IEEE Transactions on Signal Processing, vol. 68, Jul. 2020 (working draft).

33. C. Ye and G. Mateos, ``Online tensor decomposition and imputation for streaming Poisson data,'' Signal Processing, vol. 176, Article no. , Aug. 2020 (working draft).

32. A. G. Marques, S. Segarra and G. Mateos, ``Signal processing on directed graphs,'' IEEE Signal Processing Magazine, vol. 37, Nov. 2020.

31. Y. Li and G. Mateos, ``A network deconvolution approach to identification of structural brain networks from functional connectivity,'' IEEE Transactions on Medical Imaging, vol. 5, Aug. 2020 (working draft).

30. R. Shafipour and G. Mateos, ``Online topology inference from streaming stationary graph signals with partial connectivity information,'' Algorithms, vol. 13, no. 9, Sep. 2020.

29. Y. Li and G. Mateos, ``Networks of international football: Community structure, evolution and globalization of the game,'' Applied Network Science, vol. 7, no. 59, pp. 1-28, Aug. 2022.

28. A. Hashemi, R. Shafipour, H. Vikalo, and G. Mateos, ``Towards accelerated greedy sampling and reconstruction of bandlimited graph signals,'' Signal Processing, vol. 195, Article no. 108505, Feb. 2022.

27. G. Mateos, S. Segarra, A. G. Marques and A. Ribeiro, ``Connecting the dots: Identifying network structure via graph signal processing,'' IEEE Signal Processing Magazine, vol. 36, no. 3, pp. 16-43, May 2019.

26. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, ``A directed graph Fourier transform with spread frequency components,'' IEEE Transactions on Signal Processing, vol. 67, no. 4, pp. 946-960, Feb. 2019.

25. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, ``Identifying the topology of undirected networks from diffused non-stationary graph signals,'' IEEE Open Journal of Signal Processing, vol. 2, pp. 171-189, Apr. 2021.

24. R. Shafipour, R. A. Baten, M. K. Hasan, G. Ghoshal, G. Mateos, and M. E. Hoque, ``Buildup of speaking skills in an online learning community: A network-analytic exploration,'' Palgrave Communications, vol. 4, June 2018.

23. F. Gama, A. G. Marques, G. Mateos and A. Ribeiro, ``Rethinking sketching as sampling: A graph signal processing approach,'' Signal Processing, vol. 169, Article no. 107404, December 2019.

22. 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. 2020 IEEE Signal Processing Society Young Author Best Paper Award. Announcement. Reader's Choice.

21. 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.

20. 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.

19. 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.

18. 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. 2017 IEEE Signal Processing Society Young Author Best Paper Award. Announcement

17. 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.

16. 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. Reader's Choice

15. 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.

13. 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.

12. 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

11. 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.

10. 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.

9. 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.

8. 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.

7. 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.

6. 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.

5. 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.

4. 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.

3. 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, Article no. 981030, November 2009.

2. 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.

1. 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

82. M. Wasserman and G. Mateos, ``Stabilizing the Kumaraswamy distribution,'' Preprint, September 2024. (submitted)

81. S. Rey, S. S. Saboksayr, and G. Mateos, ``Non-negative weighted DAG structure learning,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Hyderabad, India, April 6-11, 2025. (submitted)

80. H. Chahuara and G. Mateos, ``Online proximal ADMM for graph learning from smooth signals,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Hyderabad, India, April 6-11, 2025. (submitted)

79. S. Rey, H. Ajorlou, and G. Mateos ``Convolutional learning on directed acyclic graphs,'' Proc. of 57th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, October 27-30, 2024.

78. B. Marenco, P. Bermolen, F. Larroca, M. Fiori, and G. Mateos ``A random dot product graph model for weighted and directed networks,'' Proc. of 57th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, October 27-30, 2024.

77. S. Sihag, G. Mateos, and A. Ribeiro, ``Towards a foundation model for brain age prediction using covariance neural networks,'' Preprint, January 2024. (submitted)

76. S. S. Saboksayr, G. Mateos, and M. Tepper, ``Block successive convex approximation for concomitant linear DAG estimation,'' Proc. IEEE Sensor Array and Multichannel Signal Process. Workshop, Corvallis, OR, July 8-11, 2024.

75. O. D. Kose, G. Mateos, and Y. Shen ``Filtering as rewiring for bias mitigation on graphs,'' Proc. IEEE Sensor Array and Multichannel Signal Process. Workshop, Corvallis, OR, July 8-11, 2024.

74. S. S. Saboksayr, G. Mateos, and M. Tepper, ``CoLiDE: Concomitant linear DAG estimation,'' Proc. Intl. Conf. Learning Representations, Vienna, Austria, May 7-11, 2024.

73. S. Sihag, G. Mateos, C. McMillan, and A. Ribeiro, ``Explainable brain age prediction using covariance neural networks,'' Proc. Neural Information Processing Systems, New Orleans, LA, December 11-14, 2023.

72. C. Ye and G. Mateos ``Online network source localization from streaming graph signals,'' Proc. of 56th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, October 29-November 1, 2023.

71. O. D. Kose, Y. Shen, and G. Mateos ``Fairness-aware graph filter design,'' Proc. of 56th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, October 29-November 1, 2023. Student paper contest finalist.

70. S. S. Saboksayr and G. Mateos, ``Dual-based online graph learning,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Rhodes Island, Greece, June 4-9, 2023.

69. S. Sihag, G. Mateos, C. McMillan, and A. Ribeiro, ``Predicting brain age using transferable covariance neural networks,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Rhodes Island, Greece, June 4-9, 2023.

68. S. Sihag, G. Mateos, C. McMillan, and A. Ribeiro, ``Covariance neural networks,'' Proc. Neural Information Processing Systems, New Orleans, LA, November 29-December 1, 2022.

67. M. Wasserman and G. Mateos, ``pyGSL: A Graph Structure Learning Toolkit,'' Proc. NeurIPS Workshop on New Frontiers in Graph Learning, New Orleans, LA, December 2, 2022. (to appear)

66. B. Marenco, F. Larroca, P. Bermolen, M. Fiori, and G. Mateos, ``Tracking the adjacency spectral embedding for streaming graphs,'' Proc. of 55th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, October 30-November 2, 2022.

65. C. Ye and G. Mateos, ``Learning to identify sources of network diffusion,'' Proc. of European Signal Process. Conf., Belgrade, Serbia, August 29-September 2, 2022.

64. Y. Li and G. Mateos, ``Learning graph-level, distance-preserving representations of brain structure-function coupling,'' Proc. of European Signal Process. Conf., Belgrade, Serbia, August 29-September 2, 2022.

63. M. Fiori, P. Bermolen, F. Larroca, B. Marenco, and G. Mateos, ``Algorithmic advances for the adjacency spectral embedding,'' Proc. of European Signal Process. Conf., Belgrade, Serbia, August 29-September 2, 2022.

62. B. Marenco, P. Bermolen, M. Fiori, F. Larroca, and G. Mateos, ``Online change-point detection for random dot product graphs,'' Proc. of 55th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, November 1-3, 2021. Student paper contest third place.

61. S. S. Saboksayr, G. Mateos, and M. Cetin, ``Fast topology identification from smooth graph signals,'' Proc. of Balkcan Conference on Commun. and Networking, Novi Sad, Serbia, September 20-22, 2021.

60. F. Larroca, P. Bermolen, M. Fiori, and G. Mateos, ``Change point detection in weighted and directed random dot product graphs,'' Proc. of European Signal Process. Conf., Dublin, Ireland, June 23-27, 2021.

59. S. S. Saboksayr, G. Mateos, and M. Cetin, ``Online graph learning under smoothness priors,'' Proc. of European Signal Process. Conf., Dublin, Ireland, August 23-27, 2021.

58. Y. Li and G. Mateos, ``Graph frequency analysis of COVID-19 incidence to identify contagion patterns in different counties of the United States,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Toronto, ON, June 6-11, 2021.

57. S. S. Saboksayr, G. Mateos, and M. Cetin, ``EEG-based emotion classification using graph signal processing,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Toronto, ON, June 6-11, 2021.

56. R. Shafipour and G. Mateos, ``Online proximal gradient for learning graphs from streaming signals,'' in Proc. of European Signal Process. Conf., Amsterdam, Netherlands, August 24-28, 2020.

55. C. Lassance, V. Gripon, and G. Mateos, ``Graph topology inference benchmarks for machine learning,'' in Proc. of IEEE Intl. Workshop on Machine Learning for Signal Processing, Espoo, Finland, September 21-24, 2020.

54. Y. Li and G. Mateos, ``Graph frequency analysis of COVID-19 prevalence in the United States,'' in Proc. of Intl. KDD Workshop on Mining and Learning with Graphs, San Diego, CA, August 24, 2020.

53. Y. Li, R. Shafipour, G. Mateos, and Z. Zhang, ``Supervised graph representation learning for modeling the relationship between structural and functional brain connectivity,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Barcelona, Spain, May 4-8, 2020.

52. R. Shafipour and G. Mateos, ``Online network topology inference with partial connectivity information,'' Proc. of 8th Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, West Indies, December 15-18, 2019.

51. Y. Li, R. Shafipour, G. Mateos, and Z. Zhang, ``Mapping brain structural connectivities to functional networks via graph encoder-decoder with interpretable latent embeddings,'' Proc. of IEEE Global Conf. on Signal and Information Processing, Ottawa, ON, Nov. 11-14, 2019.

50. C. Ye and G. Mateos, ``Online tensor decomposition and imputation for count data,'' Proc. of IEEE Data Science Workshop, Minneapolis, MN, June 2-5, 2019.

49. R. Shafipour, A. Hashemi, G. Mateos, and H. Vikalo, ``Online topology inference from streaming graph signals,'' Proc. of IEEE Data Science Workshop, Minneapolis, MN, June 2-5, 2019.

48. Y. Li and G. Mateos, ``Identifying structural brain networks from functional connectivity: A network deconvolution approach,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Brighton, UK, May 12-17, 2019. Best student paper award

47. R. Shafipour, A. Khodabakhsh, and G. Mateos, ``A windowed digraph Fourier transform,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Brighton, UK, May 12-17, 2019.

46. A. Hashemi, R. Shafipour, H. Vikalo, and G. Mateos, ``A novel scheme for support identification and iterative sampling of bandlimited graph signals,'' Proc. of IEEE Global Conf. on Signal and Information Processing, Anaheim, CA, Nov. 26-28, 2018.

45. R. Shafipour and G. Mateos, ``Spread and sparse: Learning interpretable transforms for bandlimited signals on directed graphs,'' Proc. of 52th Asilomar Conf. on Signals, Systems ans Computers, Pacific Grove, CA, Oct. 28-31, 2018.

44. C. Ye, R. Shafipour and G. Mateos, ``Blind identification of invertible graph filters with sparse inputs,'' Proc. of European Signal Processing Conference, Rome, Italy, Sep. 3-7, 2018.

43. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, ``Directed network topology inference via graph filter identification,'' Proc. of IEEE Data Science Workshop, Lausanne, Switzerland, Jun. 4-6, 2018.

42. 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.

41. 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. Best student paper award

40. 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.

39. 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.

38. 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.

37. 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.

36. 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.

35. 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.

34. 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.

33. 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.

32. 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.

31. 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

30. 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.

29. 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.

28. 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)

27. 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.

26. 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.

25. 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.

24. 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.

23. 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.

22. 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.

21. 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.

20. 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.

19. 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)

18. G. Mateos and G. B. Giannakis, ``Steady-state performance analysis of the distributed RLS algorithm,'' Proc. of Intl. Workshop on Machine Learning for Signal Processing, Santander, Spain, Sep. 23-26, 2012.

17. 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.

16. 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.

15. 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

14. 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)

13. 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)

12. 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.

11. 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.

10. 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.

9. 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

8. 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)

7. 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.

6. 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.

5. 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.

4. 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.

3. 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.

2. 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.

1. 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

2. 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.

1. 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.