Currently I am a Ph.D. candidate in Control Systems (XXX cycle) at IMT School for Advanced Studies Lucca, in the Dynamical Systems, Control, and Optimization (DYSCO) research unit, under the supervision of Prof. Alberto Bemporad and Dr. Dario Piga. From January to July 2017 I have been a visting scholar at the Department of Aerospace Engineering at University of Michigan (MI, USA), under the supervision of Prof. Ilya Kolmaovsky.

Education

I have recieved my Bachelor Degree in Electronics and Telecommunications engineering (cum laude) in December 2011 and my Master Degree in Electrical and Automation engineering (cum laude) in April 2014, both from the University of Florence (Italy).

Research Interests

My research interests include:

• hybrid and stochastic systems identification
• multi-model filtering, with applications to the energy domain
• consensus-based collaborative estimation

Publication

Journal Papers

1. V. Breschi, D. Piga, and A. Bemporad. Piecewise affine regression via recursive multiple least squares and multicategory discrimination. Automatica, 73:155–162, 2016. Available at: https://doi.org/10.1016/j.automatica.2016.07.016

Conference proceedings

1. V. Breschi, A. Bemporad, and D. Piga. Identification of hybrid and linear parameter varying models via recursive piecewise affine regression and discrimination. In Proc. of the 15th European Control Conference, pages 2632–2637, 2016. DOI:  10.1109/ECC.2016.7810687

2. V. Breschi, D. Piga, and A. Bemporad. Learning hybrid models with logical and continuous dynamics via multiclass linear separation. In 2016 IEEE 55th Conference on Decision and Control (CDC), pages 353–358, Dec 2016. DOI: 10.1109/CDC.2016.7798294

Technical reports

1. A. Bemporad, V. Breschi, D.Piga. (2016). Piecewise Affine Regression via Recursive Multiple Least Squares and Multicategory Discrimination. Technical report, TRIMT-DYSCO-2016-01. Available online at: http://www.dariopiga.com/TR/TR-IMT-DYSCO-2016-01.pdf
2. V. Breschi, I. Kolmanovsky, A. Bemporad. Cloud-aided collaborative estimation by ADMM-RLS algorithms for connected vehicle prognostics. Available online at: http://dysco.imtlucca.it/valentina/reports/CloudAided_linear.pdf ArXiv: http://arxiv.org/abs/1709.07972

Submitted 

• Journal 

1. V. Breschi, D. Piga, and A. Bemporad. Online end-use energy disaggregation via jump linear models.
2. A. Bemporad, V. Breschi, D. Piga, S. Boyd. Fitting Jump Models. Available at http://arxiv.org/abs/1711.09220

• Conference

1. V. Breschi, D. Piga, and A. Bemporad. Jump model learning and ltering for energy end-use disaggregation.
2. V. Breschi, I. Kolmanovsky, and A. Bemporad. Cloud-aided collaborative estimation by ADMM-RLS algorithms for connected vehicle prognostics. Accepted at American Contr. Conf., Milwaukee.