Skip to content. Skip to main navigation.

Biography

Ioannis D. Schizas received the diploma in Computer Engineering and Informatics (with honors) from the University of Patras, Greece, in 2004, the M.Sc. in Electrical and Computer Engineering from the University of Minnesota, Minneapolis, in 2007 and the Ph. D. in Electrical and Computer Engineering from the University of Minnesota, Minneapolis, in June 2011. Since August 2011 he has been an Assistant Professor at the Electrical Engineering department at the University of Texas at Arlington. His general research interests lie in the areas of statistical signal processing, wireless sensor networks and data dimensionality reduction. His latest research efforts focus on designing information processing techniques for handling and analyzing large amounts of spatially scattered data. His long term goal is to introduce efficient, robust and intelligent data processing algorithms that can deal with i) the constantly growing volume of data; ii) the wide spread of decentralized storage units, as well as sensing and processing systems; and iii) the heterogeneity that the available data exhibit. 

Professional Preparation

    • 2011 PhD University of Minnesota
    • 2007 MSc University of Minnesota
    • 2004 5-year Diploma in Computer Engineering and InformaticsUniversity of Patras, Greece

Appointments

    • Sept 2011 to Present Assist Professor
      University of Texas at Arlington

Memberships

  • Member
    • July 2013 to Present IEEE Geoscience and Remote Sensing
    • Sept 2011 to Present IEEE SPS
    • Sept 2011 to Present IEEE

Research and Expertise

  • Distributed statistical signal processing

    Development of distributed signal processing algorithms with applications to statistical inference, denoising, dimensionality reduction and compression.

  • Wireless sensor networks

    Development and analysis of distributed power-aware algorithms for multi-target tracking and denoising using networks of sensors.

  • Distributed identification of information-bearing sensing units in a sensor network

    This research project involves the development and analysis of efficient algorithms that uncover sparse structures in the sensor data covariance, and determine in a distributed fashion which sensors acquire informative measurements and have to remain active. Identifying the ‘informative’ sensors can lead to significant energy savings. The research focuses on the novel utilization of covariance sparsity in developing distributed informative sensor selection algorithms that have the ability to adaptively learn the statistical behavior of the sensed field, without relying on a priori known data models. 

  • Intelligent-distributed multi-threat target tracking:

    An innovative engagement of stochastic filtering techniques with sparse matrix factorization. The hybrid framework stemming from this exciting blending is applied to a wide spectrum of multi-threat localization and tracking applications ranging from defense to biochemical threat scenarios. A two-level multi-threat sparsity-aware tracking framework is being developed. In the first level, novel techniques, relying on norm-1 regularization, are being designed to analyze the sensor data covariance matrix into sparse factors. The support of these factors will be used to identify the threat-informative sensors. Once the informative sensors are identified and their corresponding measurements acquired, they will be in turn used, at the second level of our framework to accurately track the threats. The second level entails the design of majorly improved particle filtering techniques that are capable of simultaneously tracking multiple threats. Drift homotopy tools will be employed to devise improved particle sampling techniques. 

  • A Framework for Exploring Data in Heterogeneous Sensor Networks:

    The vision of this project is the development and analysis of an algorithmic framework that has the ability to learn the unknown structure of a monitored field and enable the mining and exploration of information in heterogeneous sensor data. The techniques developed in this project will enable learning of the sensed field while effectively reducing the usually large amount of sensor data that need to be processed by removing irrelevant and non-informative sensor measurements.  

Publications

      Journal Article Forthcoming
      • A. Malhotra*, G. Binetti, A. Davoudi and I. D. Schizas, “Distributed Power Profile Tracking for Heterogeneous Charging of Electric Vehicles,” IEEE PES Transactions on Smart Grid, to appear 2016. DOI: 10.1109/TSG.2016.2515616. 

        {Journal Article} [Refereed/Juried]
      • Forthcoming
        • G. Ren*, V. Maroulas and I. D. Schizas, “Exploiting Sensor Mobility and Sparse Covariances for Distributed Tracking of Multiple Targets,” EURASIP Journal on Advances in Signal Processing, vol. 53, pp. 1-15, May 2016. DOI: 10.1186/s13634-016-0354-y. 

          {Journal Article} [Refereed/Juried]
        • Forthcoming
          • S. Abhinav, I. D. Schizas, F. L. Lewis and A. Davoudi, “Distributed Noise Resilient Networked Synchrony of Active Distribution Systems,” IEEE PES Transactions on Smart Grid, to appear 2016. DOI: 10.1109/TSG.2016.2569602. 

            {Journal Article} [Refereed/Juried]

            Journal Article 2017
            • J. Chen, A. Malhotra, and I. D. Schizas, “Data-Driven Sensor Clustering and Filtering for Communication Efficient Field Reconstruction,” Elsevier Signal Processing, vol. 133, pp. 156–168, April 2017. DOI: 10.1016/j.sigpro.2016.10.024. 

              {Journal Article} [Refereed/Juried]

              Conference Paper 2016
              • G. Ren, I. D. Schizas and V. Maroulas, “Sparsity Based Multi-Target Tracking Using Mobile Sensors,” IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp. 4578-4582, March 20-25, 2016. 

                {Conference Paper} [Refereed/Juried]
              • 2016
                • K. Kang, V. Maroulas, I. D. Schizas and E. Blasch, “An Enhanced Sequential Monte Carlo Filter and its Application to Multi-Target Tracking,” Proc. of the Intl. Conf. on Information Fusion, Heidelberg, Germany, July 5-8, 2016. 

                  {Conference Paper} [Refereed/Juried]
                • 2016
                  • A. Malhotra, N. Erdogan, I. D. Schizas, A. Davoudi and G. Binetti, “Impact of Charging Interruptions in Coordinated Electric Vehicle Charging,” Proc. of the IEEE Global Conference on Signal and Information Processing, Washington, DC, Dec. 2016. (invited) 

                    {Conference Paper} [Refereed/Juried]

                  • Journal Article 2016
                    • J. Chen* and I. D. Schizas, “Online Distributed Sparsity-Aware Canonical Correlation Analysis,’’ IEEE Transactions on Signal Processing, vol. 64, no. 3, pp. 688-703, February 2016. DOI: 10.1109/TSP.2015.2481861

                      {Journal Article} [Refereed/Juried]
                    • 2016
                      • G. Ren*, V. Maroulas and I. D. Schizas, “Decentralized Sparsity-Based Multi-Source Association and State Tracking,” Elsevier Signal Processing, vol. 120, pp. 627-643, March 2016. DOI: 10.1016/j.sigpro.2015.10.013

                        {Journal Article} [Refereed/Juried]
                      • 2016
                        • J. Chen* and I. D. Schizas, “Distributed Information-Based Clustering of Heterogeneous Sensor Data,’’ Elsevier Signal Processing, vol. 126, pp. 35-51, September 2016. DOI:10.1016/j.sigpro.2015.12.017. 

                          {Journal Article} [Refereed/Juried]

                          Conference Paper 2015
                          •  G. Ren*, I. D. Schizas and V. Maroulas, “Distributed Spatio-Temporal Multi-Target Association and Tracking,” Proc. of IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, pp. 4010—4014, 2015. 

                            {Conference Paper} [Refereed/Juried]
                          • 2015
                            • J. Chen* and I. D. Schizas, “Regularized Canonical Correlations for Sensor Data Information Clustering,” Proc. of IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, pp. 3601—3605, 2015. 

                              {Conference Paper} [Refereed/Juried]
                            • 2015
                              • V. Maroulas, K. Kang, I. D. Schizas and M. W. Berry, “A Learning Drift Homotopy Particle Filter,” Proc. of the Intl. Conf. on Information Fusion, Washington, DC, pp. 1930—1937, July 6-9, 2015. 

                                {Conference Paper} [Refereed/Juried]
                              • 2015
                                • V. Metsis, I. D. Schizas and G.Marshall, “Real-time Subspace Denoising of Polysomnographic Data,” Proc. of 8th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA’15), Corfu, Greece, July 2015. DOI: http://dx.doi.org/10.1145/2769493.2769584 

                                  {Conference Paper} [Refereed/Juried]
                                • 2015
                                  • S. Abhinav*, I. D. Schizas and A. Davoudi, “Noise-Resilient Synchrony of AC Microgrids,” International Symposium on Resilient Control Systems, Philadelphia, PA, pp. 1—6, Aug. 2015. 

                                    {Conference Paper} [Refereed/Juried]
                                  • 2015
                                    • J. Chen*, A. Malhotra* and I. D. Schizas, “Information-Based Clustering and Filtering for Field Reconstruction,” Proc. of the Asilomar Conf. on Signals, Systems and Comp., Pacific Grove, CA, pp. 576-580, Nov. 8-11, 2015. 

                                      {Conference Paper} [Refereed/Juried]

                                    • Journal Article 2015
                                      • G. Ren*, V. Maroulas and I. D. Schizas, “Distributed Spatio-Temporal Association and Tracking of Multiple Targets Using Multiple Sensors,’’ IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 4, pp. 2570-2589, April 2015. 

                                        {Journal Article} [Refereed/Juried]
                                      • 2015
                                        • I. D. Schizas and V. Maroulas, “Dynamic Data Driven Sensor Network Selection and Tracking,’’ Elsevier Procedia Computer Science, vol. 51, pp. 2583--2592, 2015. 

                                          {Journal Article} [Refereed/Juried]
                                        • 2015
                                          • I. D. Schizas and A. Aduroja*, “A Distributed Framework for Dimensionality Reduction and Denoising,’’ IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6379-6394, Dec. 2015. DOI:10.1109/TSP.2015.2465300

                                            {Journal Article} [Refereed/Juried]

                                            Conference Paper 2014
                                            • G. Ren*, I. D. Schizas and V. Maroulas, “Joint Sensors-Sources Association and Tracking,’’ Proc. of the IEEE Sensor Array and Multichannel Signal Processing Workshop, A Coruna, Spain, pp. 754—758, June 22-25, 2014. 

                                              {Conference Paper} [Refereed/Juried]
                                            • 2014
                                              • K. Kang, V. Maroulas and I. D. Schizas, “Drift Homotopy Particle Filter for non-Gaussian Multi- target Tracking,” Proc. of the Intl. Conference on Information Fusion, Salamanca, Spain, pp. 1—7, July 7-10, 2014. 

                                                {Conference Paper} [Refereed/Juried]
                                              • 2014
                                                • J. Chen* and I. D. Schizas, “Adaptive Regularized Canonical Correlations in Clustering Sensor Data,” Proc. of of the Asilomar Conf. on Signals, Systems and Comp., Pacific Grove, CA, pp. 1611— 1615, Nov. 2-5, 2014. 

                                                  {Conference Paper} [Refereed/Juried]
                                                • 2014
                                                  • G. Ren* and I. D. Schizas, “Joint Sensors-Sources Association and Tracking under Power Constraints,” Proc. of the IEEE Global Conference on Signal and Information Processing, Atlanta, GA, Dec. 3-5, pp. 754—758, 2014. 

                                                    {Conference Paper} [Refereed/Juried]

                                                    Conference Paper 2013
                                                    • A. Aduroja, I. D. Schizas and V. Maroulas, ``Distributed Principal Component Analysis in Sensor Networks,'' Proc. of IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) , May 26-31, 2013.
                                                      {Conference Paper} [Refereed/Juried]
                                                    • 2013
                                                      • I. D. Schizas`` Adaptive Distributed Sparsity-Aware Matrix Decomposition,'' Proc. of IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) May 26-31, 2013.
                                                        {Conference Paper} [Refereed/Juried]
                                                      • 2013
                                                        • J. Chen and I. D. Schizas, ``Distributed Sparse Canonical Correlation Analysis in Clustering Sensor Data,'' Proc. of the Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, Nov. 2013

                                                          {Conference Paper} [Refereed/Juried]
                                                        • 2013
                                                          • I. D. Schizas, ``Distributed Data Cleansing via a Low-Rank Decomposition,'' Proc. of IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Dec.  3-5, 2013.

                                                            {Conference Paper} [Refereed/Juried]
                                                          • 2013
                                                            • G. Ren and I. D. Schizas, ``Distributed Sensor-Informative Tracking of Targets,'' Proc. of the IEEE Intl. Workshop on Comp. Advances in Multi-Sensor Adaptive Processing, Saint Martin, Dec. 2013.

                                                              {Conference Paper} [Refereed/Juried]

                                                            • Journal Article 2013
                                                              • I. D. Schizas, `` Distributed Informative-Sensor Identification using Sparsity-Aware Matrix Factorization,'' IEEE Trans. on Sig. Proc., vol. 61, no. 18, pp. 4610--4624, Sep. 2013. 

                                                                {Journal Article} [Refereed/Juried]

                                                                Conference Paper 2012
                                                                • I. D. Schizas``Distributed Informative-Sensor Determination via  Sparsity-Cognizant Matrix Decomposition,'' Proc. of IEEE Workshop on Statistical Signal ProcessingAugust 5-8, 2012.
                                                                  {Conference Paper} [Refereed/Juried]

                                                                • Journal Article 2012
                                                                  • I. D. Schizas and G. B. Giannakis, ``Covariance-Domain Sparsity for Data Compression and Denoising,'' IEEE Transactions on Signal Processing, May 2012.
                                                                    {Journal Article} [Refereed/Juried]

                                                                    Conference Paper 2011
                                                                    •  I. D. Schizas and G. B. Giannakis, `` Eigenspace Sparsity for Compression and Denoising,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Prague, Czech Republic, May 22-27, 2011. 
                                                                      {Conference Paper} [Refereed/Juried]

                                                                      Journal Article 2010
                                                                      • A. Ribeiro, I. D. Schizas, S. I. Roumeliotis and G. B. Giannakis, ``Kalman Filtering in Wireless Sensor Networks: Incorporating communication cost in state estimation problems,'' IEEE Control Systems Magazine, April 2010.
                                                                        {Journal Article} [Refereed/Juried]

                                                                        Conference Paper 2009
                                                                        • I. D. SchizasG. B. Giannakis and N. D. Sidiropoulos, ``Exploiting Covariance-domain Sparsity for Dimensionality Reduction,'' Proc. of 3rd Intl. Workshop on Comp. Advances in Multi-Sensor Adapt. Proc.ArubaIslandDec. 13-16, 2009.
                                                                          {Conference Paper} [Refereed/Juried]
                                                                        • 2009
                                                                          • G. Mateos, I. D. Schizas and G. B. Giannakis, `` Closed-form MSE perfomance of the distributed LMS algorithm,''Proc. of 13th DSP Workshop, Marco Island, FL, January 4-7, 2009.
                                                                            {Conference Paper} [Refereed/Juried]

                                                                          • Journal Article 2009
                                                                            • H. Zhu, I. D. Schizas and G. B. Giannakis, ``Power-Efficient Dimensionality Reduction for Distributed Channel-Aware Kalman Tracking Using Wireless Sensor Networks,'' IEEE Transactions on Signal Processing, August 2009. 
                                                                              {Journal Article} [Refereed/Juried]
                                                                            • 2009
                                                                              • I. D. Schizas, G. Mateos and G. B. Giannakis, `` Distributed LMS for Consensus-Based In-Network Adaptive Processing,'' IEEE Transactions on Signal Processing, June 2009.
                                                                                {Journal Article} [Refereed/Juried]
                                                                              • 2009
                                                                                • 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, Nov. 2009.
                                                                                  {Journal Article} [Refereed/Juried]
                                                                                • 2009
                                                                                  • 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, Oct. 2009.
                                                                                    {Journal Article} [Refereed/Juried]

                                                                                    Conference Paper 2008
                                                                                    • 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.
                                                                                      {Conference Paper} [Refereed/Juried]

                                                                                    • Journal Article 2008
                                                                                      • I. D. Schizas, G. B. Giannakis and N. Jindal, ``Distortion-Rate Bounds for Distributed Estimation with Wireless Sensor Networks, EURASIP Journal on Advances in Signal Processing, 2008.
                                                                                        {Journal Article} [Refereed/Juried]
                                                                                      • 2008
                                                                                        • I. D. Schizas, A. Ribeiro and G. B. Giannakis, `` Consensus in Ad Hoc WSNs with Noisy Links - Part I: Distributed Estimation of Deterministic Signals,'' IEEE Transactions on Signal Processing, January 2008.
                                                                                          {Journal Article} [Refereed/Juried]
                                                                                        • 2008
                                                                                          • I. D. Schizas, G. B. Giannakis, S. I. Roumeliotis and A. Ribeiro, ``Consensus in Ad Hoc WSNs with Noisy Links - Part II: Distributed Estimation and Smoothing of Random Signals,'' IEEE Transactions on Signal Processing, April 2008.
                                                                                            {Journal Article} [Refereed/Juried]

                                                                                            Book Chapter 2007
                                                                                            • A. Ribeiro,I.D. Schizas, J.-J. Xiao, G. B. Giannakis, and Z.-Q. Luo '' Distributed Estimation Under Bandwidth and Energy Constraints,'' in Wireless Sensor Networks: Signal Processing and Communications Perspectives (A. Swami, Q. Zhao, Y. Hong, and L. Tong, eds.), Wiley, February 2007.
                                                                                              {Book Chapter} [Refereed/Juried]

                                                                                            • Conference Paper 2007
                                                                                              • I. D. Schizas,A. Ribeiro and G. B. Giannakis, `` Consensus-Based Distributed Parameter Estimation in Ad Hoc Wireless Sensor Networks with Noisy Links,''Proc. of Intl. Conf. on Acoustics, Speech and Signal ProcessingHonolulu, HI, April 15-20, 2007.
                                                                                                {Conference Paper} [Refereed/Juried]
                                                                                              • 2007
                                                                                                • I. D. Schizas,G. B. Giannakis and A. Ribeiro, ``Distributed MAP and LMMSE Estimation of Random Signals Using Ad Hoc Wireless Sensor Networks with Noisy Links,'' Proc. of SPAWC, Helsinki, Finland, June 17- 20, 2007.
                                                                                                  {Conference Paper} [Refereed/Juried]
                                                                                                • 2007
                                                                                                  •  I. D. Schizas,G. B. Giannakis, Stergios I. Roumeliotis and A. Ribeiro, ``Any-time Optimal Distributed Kalman Filtering and Smoothing,'' Proc. of Wrkshp. on Statistical Signal Processing, Madison, WI, August 26-29, 2007.
                                                                                                    {Conference Paper} [Refereed/Juried]
                                                                                                  • 2007
                                                                                                    •  H. Zhu, I. D. Schizas andG. B. Giannakis, `` Power-Efficient Dimensionality Reduction for Distributed Channel-Aware Kalman Tracking Using Wireless Sensor Networks,'' Proc. of Wrkshp. on Statistical Signal Processing, Madison, WI, August 26-29, 2007.
                                                                                                      {Conference Paper} [Refereed/Juried]
                                                                                                    • 2007
                                                                                                      • I. D. Schizas, G. Mateos and G. B. Giannakis, ``Distributed Recursive Least-Squares Using Wireless Ad Hoc Sensor Networks,'' Proc.of 41st Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 4-7, 2007.
                                                                                                        {Conference Paper} [Non-refereed/non-juried]
                                                                                                      • 2007
                                                                                                        •  G. Mateos, I. D. Schizas and G. B. Giannakis, ``Distributed Least-Mean Square Algorithm Uisng Wireless Ad Hoc Networks,'' Proc. of 45th Allerton Conf., Univ. of Illinois at U-C, Monticello, IL, Sept. 26-28, 2007.
                                                                                                          {Conference Paper} [Refereed/Juried]

                                                                                                        • Journal Article 2007
                                                                                                          • I. D. Schizas, G. B. Giannakis and Z.-Q. Luo, ``Distributed Estimation Using Reduced-Dimensionality Sensor Observations,'' IEEE Transactions on Signal Processing, August 2007.
                                                                                                            {Journal Article} [Refereed/Juried]

                                                                                                            Book Chapter 2006
                                                                                                            • I. D. Schizas, A. Ribeiro, and G. B. Giannakis '' Dimensionality Reduction, Compression and Quantization for Distributed Estimation with Wireless Sensor Networks,'' in Wireless Communications (P. Agrawal, D. M. Andrews, P. J. Fleming, G. Yin, and L. Zhang, eds.)vol. 143 of IMA Volumes in Mathematics and its Applications, pp. 259--296, Springer, New York, 2006. 
                                                                                                              {Book Chapter} [Refereed/Juried]

                                                                                                            • Conference Paper 2006
                                                                                                              • I. D. Schizas, A. Ribeiro and G. B. Giannakis, ``Distributed Estimation with Ad-Hoc Wireless Sensor Networks,'' Proc. of XIV EuropeanConf. Signal Processing Conference, Florence, Italy, Sep. 4-8, 2006.
                                                                                                                {Conference Paper} [Refereed/Juried]
                                                                                                              • 2006
                                                                                                                • I. D. Schizas and G. B. Giannakis, `` Consensus-Based Distributed Estimation of Random Signals with Wireless Sensor Networks,'' Proc. of 40th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 29-Nov. 1, 2006.
                                                                                                                  {Conference Paper} [Refereed/Juried]
                                                                                                                • 2006
                                                                                                                  • I. D. Schizas, G. B. Giannakis and Z.-Q. Luo, ``Optimal Dimensionality Reduction for Multi-Sensor Fusion in the Presence of Fading and Noise,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Toulouse, France, May 15-19, 2006. 
                                                                                                                    {Conference Paper} [Refereed/Juried]

                                                                                                                    Conference Paper 2005
                                                                                                                    • I. D. Schizas, G. B. Giannakis and N. Jindal, ``Distortion-Rate Analysis for Distributed Estimation with Wireless Sensor Networks,'' Proc. Of 43rd Allerton Conf., Univ. of Illinois at U-C, Monticello, IL, Sept. 28-30, 2005.
                                                                                                                      {Conference Paper} [Refereed/Juried]
                                                                                                                    • 2005
                                                                                                                      • I. D. Schizas, G. B. Giannakis and Z.-Q. Luo, ``Distributed Estimation Using Reduced Dimensionality Sensor Observations,'' Proc. of 39th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 30-Nov. 2, 2005.
                                                                                                                        {Conference Paper} [Refereed/Juried]

Presentations

    • February  2015
      Distributed Sensor Data Clustering and Cleansing

      Electrical Engineering, University of North Texas, February 2015. 

    • March  2015
      Distributed Data Clustering and Cleansing in Sensor Networks

      Electrical and Computer Engineering, University of Texas at San Antonio, March 2015. 

    • November  2015
      Distributed Information Discovery in Heterogeneous Data

      Center for Intelligent Systems and Machine Learning (CISML), University of Tennessee at Knoxville, November 2015. 

    • March  2013

      Distributed Informative-Sensor Identification via Sparsity-Aware Covariance Decomposition

    • April  2013

      Distributed Determination of Informative Network Nodes via Sparsity-Cognizant Covariance Decomposition

    • November  2013

      Distributed Informative-Sensor Identification and Tracking via Sparsity-Aware Covariance Factorization

      IEEE
      Antennas and Propagation Society Technical Seminar, Fort Worth Chapter, Nov. 2013.

Support & Funding

    • July 2015 to June 2018 A Framework for Exploring Data in Heterogeneous Sensor Networks sponsored by  - $150000
    • Feb 2015 to Sept 2017 A Distributed Dynamic Data Driven Applications System (DDDAS) for Multi-Threat Tracking sponsored by  - $225000
    • Sept 2012 to Aug 2016 Distributed Informative Sensor Selection via Sparse Covariance Factorization sponsored by  - $205665
    • June 2012 to May 2013 Energy-Efficient Sensor Networks via Distributed Active Sensor Selection sponsored by  - $10000

Students Supervised

  • Doctoral
    • Present
      thumbnail

      Guohua is working on distributed multi-target tracking problems.

    • May 2016
      thumbnail

      Jia is working on sparsity-aware signal processing with applications to big data processing.

    • Present
      thumbnail

      Big data signal processing

  • Master's

Courses

      • EE 5362-001 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • EE 5362-002 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • EE 4362-001 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • EE 5369-001 Estimation Theory and Distributed Algorithms

        The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. After traditional theory is covered, the focus will move in distributed estimation with applications in sensor networks. Decentralized optimization tools such as the alternating direction method of multipliers will be studied and applied in deriving distributed estimators and tracking algorithms. Different modern distributed techniques will be considered and compared along with applications in sensing, data compression, data denoising and multi-target tracking.  The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications, especially in networks of sensors.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • EE 5362-001 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • EE 5362-002 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • EE 4328-006 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • EE 5362-001 Digital Communication

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • EE 5362-002 Digital Communication

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • EE 4328-009 Digital Communication

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • EE 5362-001 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • EE 5362-002 DIGITAL COMMUNICATIONS

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • EE 5369-001 Distributed Estimation Theory

        The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. After traditional theory is covered, the focus will move in distributed estimation with applications in sensor networks. Decentralized optimization tools such as the alternating direction method of multipliers will be studied and applied in deriving distributed estimators and tracking algorithms. Different modern distributed techniques will be considered and compared along with applications in sensing, data compression, data denoising and multi-target tracking.  The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications, especially in networks of sensors.

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • EE 5362-001 Digital Communications

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours1 Link
      • EE 5369-001 Ee 5369-001

        The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. After traditional theory is covered, the focus will move in distributed estimation with applications in sensor networks. Decentralized optimization tools such as the alternating direction method of multipliers will be studied and applied in deriving distributed estimators and tracking algorithms. Different modern distributed techniques will be considered and compared along with applications in sensing, data compression, data denoising and multi-target tracking.  The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications, especially in networks of sensors.

        Spring - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • EE 5362-001 Digital Communications

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2013 Download Syllabus Contact info & Office Hours
      • EE 5362-002 Digital Communications-Web Session

        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.

        Fall - Regular Academic Session - 2013 Download Syllabus Contact info & Office Hours
      • EE 5362-001 DIGITAL COMMUNICATIONS
        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
        Spring - Regular Academic Session - 2013 Download Syllabus 1 Link
      • EE 5368-001 WIRELESS COMMUNICATION SYSTEMS
        The course presents fundamental principles underlying the wireless transmission and reception of information, and studies the different parts of a modern wireless communication system. Specifically, the course will touch upon different digital modulation schemes, as well as the design and performance analysis of a transmission and reception end. The concept of diversity and its impact on reception performance (probability of symbol detection error) will be discussed. Channel capacity and channel coding will also be studied. Further, techniques for adaptive modulation and channel equalization used in state-of-the-art wireless systems will be presented. Communication using orthogonal frequency division multiplexing (OFDM), as well as spread spectrum techniques will also explored. Topics in multi-user systems, random access, cellular systems and ad hoc networks will also be covered. The goal of this course is to help graduate students to i) learn about different wireless communication technologies; ii) understand the basic components of a wireless communication system; iii) be able to design basic components in a wireless communication system; and iv) analyze its performance both analytically and numerically.
        Fall - Regular Academic Session - 2012 Download Syllabus
      • EE 5362-001 DIGITAL COMMUNICATIONS
        The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
        Spring - Regular Academic Session - 2012 Download Syllabus
      • EE 5369-001 Estimation Theory
        The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications.
        Fall - Regular Academic Session - 2011 Download Syllabus

Service to the Community

  • Volunteered
    • Aug 2004 to  Present Reviewer for Journals

        - IEEE Transactions on Signal Processing

      -  IEEE Transactions on Information Theory

      -  IEEE Signal Processing Letters

      -  IEEE Signal Processing Magazine

      -  IEEE Journal On Selected Areas in Communication

      -  IEEE Journal of Selected Topics in Signal Processing

      -  EURASIP Journal on Wireless Communications and Networking

      -  IEEE Transactions on Communications

      -  IEEE Transactions on Wireless Communications

      -  IEEE Transactions on Image Processing

      -  IEEE Communications Letters

      -  IEEE Transactions on Automatic Control

      -  IEEE Transactions on Robotics

      -  Elsevier Signal Processing

      -  EURASIP Journal on Advances in Signal Processing

      -  Elsevier E-Reference Signal Processing

      -  IEEE Transactions on Industrial Informatics

      -  Elsevier Journal of The Franklin Institute

    • Aug 2004 to  Present Reviewer for conferences

      - IEEE International Conference on Communications

      - IEEE Conference on Decision and Control (CDC)
      - European Signal Processing Conference (EUSIPCO)
      -IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive 

      Processing (CAMSAP 2013)
      - IEEE Global Conference on Signal and Information Processing
      - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 

Other Service Activities

  • Uncategorized
    • Dec  Reviewer for Journals
      IEEE Transactions on Signal Processing, IEEE Journal Of Selected Topics of Signal Processing IEEE Transactions on Image Processing, IEEE Transactions on Information Theory, IEEE Transactions on Communications, IEEE Journal on Selected Areas in Communications, IEEE Communications Letters, IEEE Signal Processing Letters, IEEE Signal Processing Magazine, IEEE Transactions on Robotics, Elsevier Signal Processing, EURASIP Journal on Advances in Signal Processing, EURASIP Journal on Wireless Communications and Networking