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Alessandro Chiuso

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Professor

 

Department of Information Engineering

University of Padova


Via Gradenigo 6/b

35131 Padova, Italy

Tel.: +39 049.8277709

Fax: +39 049.827.7799

 

 

 

E-mail: tl_files/utenti/alessandrochiuso/Download/email_jpg.jpg

Webpage: http://automatica.dei.unipd.it/people/chiuso.html

Google Scholar Profile

Scopus Profile

 

 

 

   NEWS

 

 

 

   EDUCATION

 

  • Post Doctoral Fellow, Dept. of Mathematics, KTH, Stockholm, Sweden. March 2000, July 2000.
  • Ph.D., Systems Engineering, University  of Bologna, 2000
  • Visiting Research Scholar, Washington University St. Louis (USA). Aug. 1998-Jun 1999.
  • Laurea (MS), Telecommunication Engineering, University of Padova, 1996

 

   AWARDS

 

  • Ing. Aldo Gini Foundation Fellowship, Padova, Italy, 1999
  • IEEE Senior Member, 2006
  • Outstanding Reviewer, Automatica, 2007
  • Outstanding Reviewer, IEEE Transactions on Automatic Control, 2009
  • Best Oral Presentation, SIDRA meeting 2012

 

   PROJECTS

 

ONGOING:


  • (Coordinator) FIRB 2012 (MIUR): Learning meets time, a new computational approach to learning in dynamic systems (UNIPD - UNIGE - UNIFE, total funding 715 Keuro) 
  • (Coordinator) Progetto SID 2016 (BIRD162411/16) "Statistical learning methods for estimating the effective connectivity of human brain networks" (total funding 51Keuro)
  • (UNIPD/Italy Team Leader) ERNSI - European Research Network on System Identification (http://people.kth.se/~bo/ERNSI/index.html)

 

PAST:

 

  • (Coordinator) Progetto di Ateneo (UNIPD): Learning methods and software for identification and estimation of large-scale distributed dynamic systems (tot. funding 38 Keuro) 
  • (Participant) HYCON II - Network of Excellence (EU Project): Highly-complex and networked control systems
  • (Participant) Feednetback (EU Project): Feedback design for wireless networked systems
  • (Participant) Recsys (EU Project): Real-Time Embedded Control of Mobile Systems with Distributed Sensing
  • (Participant) PRIN (MIUR): bi-annual projects 2000-2002-2004-2006-2008

 

 

   EDITORIAL ACTIVITY

 

 

Recent Papers     [ all BibTeX ]
20XX
M. Zorzi, A. Chiuso. The Harmonic Analysis of Kernel Functions. Submitted, 20XX   [BibTeX]  
2017
G. Prando, M. Zorzi, A. Bertoldo, A. Chiuso. Estimating effective connectivity in linear brain network models. 56th IEEE Conference on Decision and Control, pp. accepted, 2017   [BibTeX]  
S. Dey, A. Chiuso, L. Schenato. Feedback Control over lossy SNR-limited channels: linear encoder-decoder-controller design. IEEE Transactions on Automatic Control, 2017    [url] [BibTeX]  
G. Prando, G. Pillonetto, A. Chiuso. Maximum Entropy Vector Kernels for MIMO system identification. Automatica (accepted as regular paper), 2017    [url] [BibTeX]  
M. Zorzi, A. Chiuso. Sparse plus Low rank Network Identification: A Nonparamteric Approach. Automatica, vol. 53(2), 2017   [BibTeX]  
2016
G. Prando, D. Romeres, G. Pillonetto, A. Chiuso. Classical vs. Bayesian methods for linear system identification: point estimators and confidence sets. Proc. of ECC 2016, 2016   [BibTeX]  
T. Chen, G. Pillonetto, A. Chiuso, L. Ljung. DC kernel - a stable generalized first order spline kernel. Proc. of CDC 2016 - accepted, 2016   [BibTeX]  
T. Chen, T. Ardeshiri, F.P. Carli, A. Chiuso, L. Ljung, G. Pillonetto. Maximum entropy properties of discrete-time first-order stable spline kernel. Automatica, 2016   [BibTeX]  
S. Soatto, A. Chiuso. Modeling Visual Representations:Defining Properties and Deep Approximations. International Conference on Learning Representation (ICLR), 2016   [BibTeX]  
D. Romeres, G. Prando, G. Pillonetto, A. Chiuso. On-line Bayesian System Identification. Proc. of ECC 2016, 2016   [BibTeX]  
G. Prando, D. Romeres, A. Chiuso. On-line Identification of Time-Varying Systems: a Bayesian approach. IEEE CDC 2016 - accepted, 2016   [BibTeX]  
D. Romeres, M. Zorzi, R. Camoriano, A. Chiuso. Online semi-parametric learning for inverse dynamics modeling. 55th IEEE Conference on Decision and Control, 2016   [BibTeX]  
A. Chiuso. Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future. Annual Reviews in Control - in press, 2016    [url] [BibTeX]  
G. Pillonetto, T. Chen, A. Chiuso, G. De nicolao, L. Ljung. Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint. Automatica, 2016    [url] [BibTeX]  
G. Rallo, S. Formentin, A. Chiuso, S. Savaresi. Virtual Reference Feedback Tuning with bayesian regularization. ECC 2016, 2016   [BibTeX]  
2015
M. Zorzi, A. Chiuso. A Bayesian Approach to Sparse plus Low rank Network Identification. IEEE CDC 2015, 2015   [BibTeX]  
M. Zorzi, A. Chiuso. A Bayesian approach to sparse plus low rank network identification. CFE-CMStatistics 2015 Book of Abstracts, 2015    [url] [BibTeX]  
S. Bonettini, A. Chiuso, M. Prato. A SCALED GRADIENT PROJECTION METHOD FOR BAYESIAN LEARNING IN DYNAMICAL SYSTEMS. SIAM Journal on Scientific Computing (accepted), 2015   [BibTeX]  
D. Romeres, G. Pillonetto, A. Chiuso. Identification of stable models via nonparametric prediction error methods. Proc. of the European Control Conference, 2015    [ abstract ] [BibTeX]  
S. Dey, A. Chiuso, L. Schenato. Linear Encoder-Decoder-Controller Design over Channels with Packet Loss and Quantization Noise. European Control Conference ECC15, 2015   [pdf] [BibTeX]  
G. Prando, A. Chiuso. Model reduction for linear Bayesian System Identification. IEEE CDC 2015, 2015   [BibTeX]  
G. Prando, G. Pillonetto, A. Chiuso. On the role of rank penalties in linear system identification. Prof. of SYSID 2015, 2015   [BibTeX]  
A. Chiuso. Regularization and Bayesian Learning in dynamical systems: past, present and future. Plenary Lecture, SYSID 2015, 2015   [BibTeX]  
K. Tsotsos, A. Chiuso, S. Soatto. Robust Inference for Visual-Inertial Sensor Fusion. ICRA 2015 (accepted), 2015   [BibTeX]  
T. Chen, G. Pillonetto, A. Chiuso, L. Ljung. Spectral analysis of the DC kernel for regularized system identification. IEEE CDC 2015, 2015   [BibTeX]  
G. Georgiadis, A. Chiuso, S. Soatto. Texture Representations for Image and Video Synthesis. Proc. of CVPR 2015, 2015   [BibTeX]  
G. Pillonetto, A. Chiuso. Tuning complexity in regularized kernel-based regression and linear system identification: the robustness of the marginal likelihood estimator. Automatica (accepted), 2015   [BibTeX]  
S. Soatto, A. Chiuso. Visual Scene Representations: Sufficiency, Minimality, Invariance and Deep Approximation. International Conference on Learning Representation (ICLR), Workshop Track, 2015   [BibTeX]  
2014
T. Chen, M. Andersen, A. Chiuso, G. Pillonetto, L. Ljung. Anomaly detection in homogenous populations: a sparse multiple kernel-based regularization method. IEEE CDC 2014, 2014   [BibTeX]  
A. Chiuso, G. Pillonetto. Bayesian and nonparametric methods for system identification and model selection. Proc. of ECC 2014, 2014   [BibTeX]  
G. Prando, A. Chiuso, G. Pillonetto. Bayesian and regularization approaches to multivariable linear system identification: the role of rank penalties. Proc. IEEE CDC, 2014   [BibTeX]  
A. Aravkin, J. Burke, A. Chiuso, G. Pillonetto. Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso. Journal of Machine Learning Research, (15), pp. 1-36, 2014   [BibTeX]  
A. Chiuso, N. Laurenti, L. Schenato, A. Zanella. LQG-like control of scalar systems over communication channels: the role of data losses, delays and SNR limitations. Automatica, vol. 50(12), pp. 3155–3163, 2014    [url] [pdf] [BibTeX]  
A. Chiuso, T. Chen, L. Ljung, G. Pillonetto. On the design of Multiple Kernels for nonparametric linear system identification. IEEE CDC 2014, 2014   [BibTeX]  
S. Dey, A. Chiuso, L. Schenato. Remote estimation with noisy measurements subject to packet loss and quantization noise. IEEE Transactions on Control of Network Systems, vol. 1(3), pp. 204-217, 2014    [url] [pdf] [BibTeX]  
A. Chiuso. System Identification Techniques: Convexification, Regularization, Relaxation. Springer Encyclopedia of Systems and Control, 2014   [BibTeX]  
T. Chen, M. Andersen, L. Ljung, A. Chiuso, G. Pillonetto. System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques. IEEE Transactions on Automatic Control, 2014   [BibTeX]  
G. Pillonetto, A. Chiuso. Tuning complexity in kernel-based linear system identification: the robustness of the marginal likelihood estimator. Proc. of ECC 2014, 2014   [BibTeX]