Our research

Our innovative solutions are rooted in years of applied research on topics spanning machine learning, statistical signal processing (estimation and detection theory), and nonlinear optimization. On par with contemporary advances in science and technology, applications of our research arise in diverse domains, including but not limited to, complex networks (social, biological, and engineered), graph-based information processing, power systems (especially integration of renewables and smart grid applications), and wireless communications. Together with collaborators across both academia and industry, the following list of papers represents some of our contributions to science and engineering.

Journal papers

• Y. Shen, G. B. Giannakis and B. Baingana, "Nonlinear Structural Vector Autoregressive Models with Application to Directed Brain Networks," IEEE Transactions on Signal Processing, 67, 20, pp. 5325-5339, October 2019.

• Y. Shen, B. Baingana and G. B. Giannakis, "Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks," IEEE Transactions on Signal Processing, 65, 14, pp. 3675 - 3687, July 2017.

• Y. Shen, B. Baingana and G. B. Giannakis, "Kernel-based Structural Equation Models for Topology Identification of Directed Networks," IEEE Transactions on Signal Processing, 65, 10, pp. 2503-2516, May 2017.

• B. Baingana and G. B. Giannakis, "Tracking Switched Dynamic Network Topologies from Information Cascades," IEEE Transactions on Signal Processing, 65, 4, pp. 985-997, February 2017.

• B. Baingana and G. B. Giannakis, "Joint Community and Anomaly Tracking in Dynamic Networks," IEEE Transactions on Signal Processing, 68, 8, pp. 2013-2025, April 2016.

• 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, 8, 4, pp. 563-575, August 2014.

Conference papers

• Shen, B. Baingana and G. B. Giannakis, "Topology Inference of Directed Graphs using Nonlinear Structural Vector Autoregressive Models," Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, New Orleans, USA, March 5-9, 2017 (invited).

• F. Sheikholeslami, B. Baingana, G. B. Giannakis and N. D. Sidiropoulos, "Egonet tensor decomposition for community identification," Proc. of Globalsip, Washington, DC, Dec. 7-9, 2016.

• Y. Shen, B. Baingana and G. B. Giannakis, "Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization," Proc. of Globalsip, Washington, DC, Dec. 7-9, 2016.

• Y. Shen, B. Baingana and G. B. Giannakis, "Inferring Directed Network Topologies via Tensor Factorization," Proc. of Asilomar Conf., Pacific Grove, CA, Nov. 6-9, 2016.

• Y. Shen, B. Baingana and G. B. Giannakis, "Nonlinear Structural Equation Models for Network Topology Inference," Proc. of Conf. on Info. Sciences and Systems, Princeton, NJ, March 16-18, 2016.

• B. Baingana and G. B. Giannakis, "Switched Dynamic Structural Equation Models for Tracking Social Network Topologies," Proc. of Globalsip Conf., Orlando, FL, Dec. 14-16, 2015. (Best paper award)

• B. Baingana and G. B. Giannakis, "Dynamic and Decentralized Learning of Overlapping Communities," Proc. 6th Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancoun, Mexico, Dec. 13-16, 2015.

• B. Baingana, E. Dall'Anese, G. Mateos and G. B. Giannakis, "Robust Kriged Kalman Filtering," Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, November 8-11, 2015.

• B. Baingana and G. B. Giannakis, "Kernel-based Embeddings for Large Graphs with Centrality Constraints," Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Brisbane, Australia, April 19-24, 2015.

• B. Baingana and G. B. Giannakis, "Tracking anomalous community memberships in time-varying networks," Proc. of GlobalSIP, Atlanta, GA, December 3-5, 2014.

• B. Baingana, G. Mateos and G. B. Giannakis, "A Proximal Gradient Algorithm for Tracking Cascades Over Networks," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Florence, Italy, May 4-9, 2014.

• B. Baingana, G. Mateos and G. B. Giannakis, "Dynamic Structural Equation Models for Tracking Cascades Over Social Networks," Proc. of Neural Info. Proc. Systems, Lake Tahoe, December 2013.

• B. Baingana, J. A. Bazerque and G. B. Giannakis, "Identifiability of Sparse Structural Equation Models for Directed, Cyclic, and Time-varying Networks," Proc. of Global Conf. on Signal and Info. Processing, Austin, TX, December 3-5, 2013.

• 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, December 15-18, 2013.

• B. Baingana and G. B. Giannakis, "Centrality-Constrained Graph Embedding," Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Vancouver, Canada, May 26-31, 2013.

Book Chapters

B. Baingana, P. A. 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.