headshot of Juan Bazerque Giusto

Juan Bazerque Giusto

Visiting Assistant Professor
juanbazerque@pitt.edu
Electrical and Computer Engineering

overview

Juan Andres Bazerque received the B.Sc. degree in electrical engineering from Universidad de la Republica (UdelaR), Montevideo, Uruguay, in 2003, ´and the M.Sc. and PhD degrees from the Department of Electrical and Computer Engineering, University of Minnesota (UofM), Minneapolis, in 2010 and 2013 respectively.

After his PhD studies he entered the Department of Electrical Engineering at UdelaR as an Assistant Professor. In 2022 he moved back to the US where he joined the Department of Electrical and Computer Engineering at the University of Pittsburgh.

His current research interests include machine learning, stochastic optimization, and networked systems, focusing on reinforcement learning, swarm robotics, graph signal processing, and power systems optimization.

Dr. Bazerque is the recipient of the UofM’s Master Thesis Award 2009-2010, and co-recipient of the best paper award at the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communication 20007.

about

Castellano, A., Min, H., Bazerque, J.A., & Mallada, E. (2023). Learning to Act Safely With Limited Exposure and Almost Sure Certainty. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 68(5), 2979-2994.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TAC.2023.3240925.

Paternain, S., Bazerque, J.A., & Ribeiro, A. (2022). Policy Gradient for Continuing Tasks in Discounted Markov Decision Processes. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 67(9), 4467-4482.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TAC.2022.3163085.

Cervino, J., Bazerque, J.A., Calvo-Fullana, M., & Ribeiro, A. (2021). Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 69, 5947-5962.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSP.2021.3122303.

Paternain, S., Bazerque, J.A., Small, A., & Ribeiro, A. (2021). Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 66(8), 3429-3444.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TAC.2020.3029317.

Nesmachnow, S., Iturriaga, S., Muraña, J., de Oca, S.M., Belcredi, G., Monzón, P., Belzarena, P., & Bazerque, J. (2019). Demand Response and Ancillary Services for Supercomputing and Datacenters. In Communications in Computer and Information Science. (pp. 203-217).Springer International Publishing. doi: 10.1007/978-3-030-38043-4_17.

Bazerque, J.A., & Giannakis, G.B. (2013). Nonparametric Basis Pursuit via Sparse Kernel-Based Learning. IEEE SIGNAL PROCESSING MAGAZINE, 30(4), 112-125.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/MSP.2013.2253354.

Bazerque, J.A., Mateos, G., & Giannakis, G.B. (2013). Rank Regularization and Bayesian Inference for Tensor Completion and Extrapolation. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 61(22), 5689-5703.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSP.2013.2278516.

Cai, X., Bazerque, J.A., & Giannakis, G.B. (2013). Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations. PLOS COMPUTATIONAL BIOLOGY, 9(5), e1003068.Public Library of Science (PLoS). doi: 10.1371/journal.pcbi.1003068.

Dall’Anese, E., Bazerque, J.A., & Giannakis, G.B. (2012). Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers. Physical Communication, 5(2), 161-172.Elsevier BV. doi: 10.1016/j.phycom.2011.07.005.

Bazerque, J.A., Mateos, G., & Giannakis, G.B. (2011). Group-Lasso on Splines for Spectrum Cartography. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 59(10), 4648-4663.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSP.2011.2160858.

Angelosante, D., Bazerque, J.A., & Giannakis, G.B. (2010). Online Adaptive Estimation of Sparse Signals: Where RLS Meets the l1-Norm. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 58(7), 3436-3447.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSP.2010.2046897.

Bazerque, J.A., & Giannakis, G.B. (2010). Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 58(3), 1847-1862.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSP.2009.2038417.

Mateos, G., Bazerque, J.A., & Giannakis, G.B. (2010). Distributed Sparse Linear Regression. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 58(10), 5262-5276.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSP.2010.2055862.

Bazerque, J.A., & Giannakis, G.B. (2008). Distributed scheduling and resource allocation for cognitive OFDMA radios. MOBILE NETWORKS & APPLICATIONS, 13(5), 452-462.Springer Science and Business Media LLC. doi: 10.1007/s11036-008-0083-z.

Castellano, A., Bazerque, J., & Mallada, E. (2021). Learning to be safe, in finite time. In 2021 American Control Conference (ACC), cs lg, (pp. 909-916).IEEE. doi: 10.23919/acc50511.2021.9482829.

Castellano, A., Bazerque, J., & Mallada, E. (2021). Model-free safe policy learning via hard action barrier functions. In 2021 55th Annual Conference on Information Sciences and Systems (CISS), (p. 1).IEEE. doi: 10.1109/ciss50987.2021.9400210.

Cervino, J., Bazerque, J.A., Calvo-Fullana, M., & Ribeiro, A. (2021). Multi-task Supervised Learning via Cross-learning. In 2021 29th European Signal Processing Conference (EUSIPCO), 31, (pp. 1381-1385).IEEE. doi: 10.23919/eusipco54536.2021.9615939.

Castellano, A., & Bazerque, J.A. (2020). Learning the operation of energy storage systems from real trajectories of demand and renewables. In 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), ii, (pp. 1-5).IEEE. doi: 10.1109/isgt45199.2020.9087648.

Castellano, A., Martinez, C., Monzon, P., Andres Bazerque, J., Ferragut, A., & Paganini, F. (2020). Quadratic approximate dynamic programming for scheduling water resources: a case study. In 2020 IEEE PES Transmission & Distribution Conference and Exhibition - Latin America (T&D LA), i, (pp. 1-6).IEEE. doi: 10.1109/tdla47668.2020.9326171.

Cervino, J., Bazerque, J.A., Calvo-Fullana, M., & Ribeiro, A. (2019). Meta-Learning through Coupled Optimization in Reproducing Kernel Hilbert Spaces. In 2019 American Control Conference (ACC), (pp. 4840-4846).IEEE. doi: 10.23919/acc.2019.8814419.

Paternain, S., Bazerque, J.A., Small, A., & Ribeiro, A. (2019). Policy Improvement Directions for Reinforcement Learning in Reproducing Kernel Hilbert Spaces. In 2019 IEEE 58th Conference on Decision and Control (CDC), 8, (pp. 7454-7461).IEEE. doi: 10.1109/cdc40024.2019.9029198.

Bazerque, J.A. (2018). Stochastic Optimization of Power Systems with Risk Constraints And Sparsely Distributed Storage. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1, (pp. 3824-3828).IEEE. doi: 10.1109/icassp.2018.8461544.

Bazerque, Juan Andres, Monzon, Pablo, & Giusto, Alvaro. (2018). Online prediction of power system trajectories from phasor measurement unit (PMU) data. In Procedings do XXII Congresso Brasileiro de Autom�tica.SBA Sociedade Brasileira de Automática. doi: 10.20906/cps/cba2018-0324.

Paternain, S., Andres Bazerque, J., Small, A., & Ribeiro, A. (2018). Learning Policies for Markov Decision Processes in Continuous Spaces. In 2018 IEEE Conference on Decision and Control (CDC), 1, (pp. 4751-4758).IEEE. doi: 10.1109/cdc.2018.8619719.

Bazerque, J.A., & Monzon, P. (2017). Control of networked systems in the graph-frequency domain. In 2017 51st Asilomar Conference on Signals, Systems, and Computers, 1, (pp. 1444-1448).IEEE. doi: 10.1109/acssc.2017.8335594.

Bazerque, J.A., & Bevc, A. (2016). Robust coordinated time for power substation networks via ℓ<inf>1</inf>-norm regularization. In 2016 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS), 58, (pp. 1-5).IEEE. doi: 10.1109/ispcs.2016.7579500.

Bazerque, J.A., Monzon, P., Pena, P., & Giusto, A. (2015). Online prediction of power system trajectories from noisy data by penalized least-squares minimization. In 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), (pp. 497-502).IEEE. doi: 10.1109/isgt-la.2015.7381205.

Bazerque, J.A., Ribeiro, U., & Costa, J. (2015). Synchronization of phasor measurement units and its error propagation to state estimators. In 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), (pp. 508-513).IEEE. doi: 10.1109/isgt-la.2015.7381207.

Bazerque, J.A., Baingana, B., & Giannakis, G.B. (2013). Identifiability of sparse structural equation models for directed and cyclic networks. In 2013 IEEE Global Conference on Signal and Information Processing, (pp. 839-842).IEEE. doi: 10.1109/globalsip.2013.6737022.

Bazerque, J.A., Mateos, G., & Giannakis, G.B. (2013). Inference of Poisson count processes using low-rank tensor data. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 54, (pp. 5989-5993).IEEE. doi: 10.1109/icassp.2013.6638814.

Bazerque, J.A., Mateos, G., & Giannakis, G.B. (2012). Nonparametric low-rank tensor imputation. In 2012 IEEE Statistical Signal Processing Workshop (SSP), 10, (pp. 876-879).IEEE. doi: 10.1109/ssp.2012.6319847.

Bazerque, J.A., Mateos, G., & Giannakis, G.B. (2011). Basis pursuit for spectrum cartography. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (pp. 2992-2995).IEEE. doi: 10.1109/icassp.2011.5946287.

Cai, X., Bazerque, J.A., & Giannakis, G.B. (2011). Gene network inference via sparse structural equation modeling with genetic perturbations. In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), 298, (pp. 66-69).IEEE. doi: 10.1109/gensips.2011.6169445.

Dall'Anese, E., Bazerque, J.A., Zhu, H., & Giannakis, G.B. (2011). Group sparse total least-squares for cognitive spectrum sensing. In 2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, 9, (pp. 96-100).IEEE. doi: 10.1109/spawc.2011.5990487.

Mateos, G., Bazerque, J.A., & Giannakis, G.B. (2011). Parallelizable algorithms for the selection of grouped variables. In 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 33, (pp. 295-300).IEEE. doi: 10.1109/dsp-spe.2011.5739228.

Bazerque, J.A., Mateos, G., & Giannakis, G.B. (2010). Distributed Lasso for in-network linear regression. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 4, (pp. 2978-2981).IEEE. doi: 10.1109/icassp.2010.5496140.

Angelosante, D., Bazerque, J.A., & Giannakis, G.B. (2009). Online coordinate descent for adaptive estimation of sparse signals. In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 58, (pp. 369-372).IEEE. doi: 10.1109/ssp.2009.5278561.

Mateos, G., Bazerque, J.A., & Giannakis, G.B. (2009). Spline-based spectrum cartography for cognitive radios. In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, (pp. 1025-1029).IEEE. doi: 10.1109/acssc.2009.5470044.

Bazerque, J.A., & Giannakis, G.B. (2008). Distributed spectrum sensing for cognitive radios by exploiting sparsity. In 2008 42nd Asilomar Conference on Signals, Systems and Computers, (pp. 1588-1592).IEEE. doi: 10.1109/acssc.2008.5074690.

Bazerque, J.A., & Giannakis, G.B. (2007). Distributed Scheduling and Resource Allocation for Cognitive OFDMA Radios. In 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, (pp. 343-350).IEEE. doi: 10.1109/crowncom.2007.4549822.

Bazerque, J., Ciambelli, J., Lafon, S., & Randall, G. (2003). Automatic dark fibers detection in wool tops. In PROGRESS IN PATTERN RECOGNITION, SPEECH AND IMAGE ANALYSIS, 2905, (pp. 367-374).Springer Berlin Heidelberg. doi: 10.1007/978-3-540-24586-5_45.

Giannakis, G., Bazerque Giusto, J., & Mateos, G. Non-parametric power spectral density (PSD) map construction. 9,191,831.

Giannakis, G., Dall'Anesse, E., Bazerque Giusto, J., Zhu, H., & Mateos, G. Robust parametric power spectral density (PSD) construction. 9,363,679.