Researcher – ISSIA CNR  Genova 

SHORT BIO

Cristiano Cervellera (born 14 november 1974 in Genova, Italy) received the M.Sc. Degree “summa cum laude” in Electronic Engineering and the Ph.D. Degree in Electronic Engineering and Computer Science from the University of Genoa, Genoa, Italy, in 1998 and 2002, respectively. 

Since 2010 he is responsible for the CNR research group “Monitoring, Optimization and Control of Complex Systems” and, since 2009, he is also responsible for the Complex Systems Laboratory of the Institute of Intelligent Systems for Automation.

He has scientific responsibility for ISSIA-CNR of many funded research projects, in different fields such as, e.g., fault detection and optimization of power plants and hybrid vehicles, container port terminal logistics, infomobility, urban traffic prediction and decision support systems.

The research activity has led to publication of more than 70 papers in international journals and conferences.
The main effort of the research activity is devoted to the development of computationally efficient algorithms of learning from data for the solution of statistical learning and optimal control problems. To this purpose, efficient sampling of the input space coming from number-theoretic and quasi-Monte Carlo methods, based on special point sets such as low-discrepancy sequences and lattice rules, has been tested in the context of neural network training, leading to a deterministic version of the classic statistical learning theory that is characterized by very favourable convergence rates. Such framework has been applied with success to nonlinear regression and to Markovian decision processes through dynamic programming.
Then, a more general learning context has been addressed by deterministic learning to solve general functional optimization problems that are the paradigm for many common instances such as dynamic programming, reinforcement learning, density estimation, etc. For instance, the approach has been applied to derive a method to obtain maximum likelihood estimators for any desired density function. Recently, semi-local approximation based on kernel structures and non-parametric models such as regression trees have been tested in the context of the aforementioned approach to furtherly improve the computational effectiveness.

From 2004 to 2011 he has been teacher of the PhD course “Statistics and Nonlinear Regression” at the University of Genova, which includes 9 Ph.D. curricula in the areas of Applied Mathematics, Operations Research, and Engineering. Currently he is teacher of the Course “Optimization and Statistics for Learning from Data” at the University of Genova.

Currently he serves as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems.

JOURNAL PUBLICATIONS

  1. C. Cervellera; M. Gaggero; D. Macciò

    Lattice point sets for state sampling in approximate dynamic programming, Optimal Control Applications & Methods, DOI: 10.1002/oca.2325, March 2017

  2. Cervellera C.; Maccio D.

    Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines, IEEE Transactions on Neural Networks and Learning Systems, Vol. 27, No. 4, 2016

  3. Cervellera C.; Macciò D.

    F-Discrepancy for Efficient Sampling in Approximate Dynamic Programming, IEEE Transactions on Cybernetics, DOI 10.1109/TCYB.2015.2453123, 2016

  4. A. Alessandri; C. Cervellera; M. Gaggero

    Nonlinear predictive control of container flows in maritime intermodal terminals, IEEE transactions on control systems technology, Vol. 21, No. 4, 1423-1431, 2013

  5. C., Cervellera; D., Macciò

    Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy, IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 11, 2014

  6. C. Cervellera, M. Gaggero and D. Macciò

    Low-Discrepancy Sampling for Approximate Dynamic Programming with Local Approximators'', Computers and Operations Research, vol. 43, pp. 108-115, 2014

  7. A. Alessandri, C. Cervellera and M. Gaggero

    Predictive Control of Container Flows in Maritime Intermodal Terminals, IEEE Transactions on Control Systems Technology, vol. 21, no. 4, pp. 1423-1431, 2013

  8. C. Cervellera and D. Macciò

    Learning with Kernel Smoothing Models and Low Discrepancy Sampling, IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 504-509, 2013

  9. C. Cervellera, M. Gaggero, D. Macciò

    Efficient Kernel Models for Learning and Approximate Minimization Problems, Neurocomputing, vol. 97, pp. 74-85, 2012

  10. D. Macciò and C. Cervellera

    Local Models for Data-Driven Learning of Control Policies for Complex Systems, Expert Systems with Applications, vol. 39, pp. 13399-13408, 2012

  11. C. Cervellera and D. Macciò

    A comparison of global and semi-local approximation in T-stage stochastic optimization, European Journal Of Operational Research, vol. 208, no. 2, pp. 109-118, 2011

  12. L. Caviglione and C. Cervellera

    An Optimized Content Replication and Distribution Framework for Vehicular Networks, Journal of Intelligent Transportation Systems, vol. 15, no. 4, pp. 179-192, 2011

  13. L. Caviglione and C. Cervellera

    Design, Optimization and Performance Evaluation of a Content Distribution Overlay for Streaming, Computer Communications, vol. 34, no. 2, pp. 1497-1509, 2011

  14. C. Cervellera and D. Macciò

    A numerical method for minimum distance estimation problems, Journal of Multivariate Analysis, vol. 102, pp. 789-800, 2011

  15. C. Cervellera, D. Macciò and M. Muselli

    Functional Optimization through Semi-Local Approximate Minimization, Operations Research, vol. 58, pp. 1491-1504, 2010

  16. Cristiano Cervellera

    Lattice point sets for deterministic learning and approximate optimization problems, IEEE Transactions on neural networks, Vol. 21, No. 4, 2010

  17. M. Baglietto, C. Cervellera, M. Sanguineti and R. Zoppoli

    Management of water resources systems in the presence of uncertainties by nonlinear approximators and deterministic sampling techniques, Computational optimization and applications, Vol. 47, 2010

  18. A. Alessandri; C. Cervellera; D. Macciò; M. Sanguineti

    Optimization based on quasi-Monte Carlo sampling to design state estimators for non-linear systems, Optimization, Vol. 59, 2010

  19. C. Cervellera; D. Macciò; M. Muselli

    Functional optimization through semilocal approximate minimization, Operations research, Vol. 58, No. 5, 2010

  20. C. Cervellera; D. Macciò; M. Muselli

    Efficient global maximum likelihood estimation through kernel methods, Neural Networks, Vol. 23, No. 7, 2010

  21. C. Cervellera and L. Caviglione

    Optimization of a peer-to-peer system for efficient content replication, European Journal of Operational Research, vol. 196, no.2, pp. 423-433, 2009

  22. A. Alessandri, C. Cervellera, M. Cuneo, M. Gaggero and G. Soncin

    Management of logistics operations in intermodal terminals by using dynamic modelling and nonlinear programming, Maritime Economics & Logistics, vol. 11, no.1, pp. 58-76, 2009

CONFERENCE PUBLICATIONS

  1. C. Cervellera; M. Gaggero; D. Macciò; R. Marcialis

    Lattice point sets for efficient kernel smoothing models, International Joint Conference on Neural Networks (IJCNN2015), Killarney, Ireland, 2015

  2. C. Cervellera; M. Gaggero; D. Maccio; R. Marcialis

    Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming, International Joint Conference on Neural Networks (IJCNN2015), Killarney, Ireland, 2015

  3. N. P. Bianchi; C. Cervellera; M. Gaggero; R. Marcialis; M. Penco; F. Sozzi

    Group Model Building and Computational Intelligence Tools for Stakeholder Engagement and Deliberative Processes, Proceedings of the Business Systems Laboratory 3rd International Symposium "Advances in Business Management. Towards Systemic Approach", 21-23 January 2015, Perugia, Italy

  4. Cervellera C.; Gaggero M.; Maccio D.; Marcialis R.

    (2014) "An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models", IEEE International Joint Conference on Neural Network proceedings, Beijing, July 6-11, 2014

  5. Cervellera C.; Gaggero M.; Maccio D.

    (2014) "An analysis based on F-discrepancy for sampling in regression tree learning", IEEE International Joint Conference on Neural Network proceedings, Beijing, China, July 6-11, 2014

  6. Cervellera C.; Gaggero M.; Maccio D.; Marcialis R.

    Lattice sampling for efficient learning with Nadaraya-Watson local models, Proceedings of the International Joint Conference on Neural Networks, Beijing, China, July 6-11, 2014

  7. C. Cervellera; V. C P. Chen; D. Macciò

    "Global and semilocal estimation in multistage optimal control", Informs Annual Meeting, Charlotte, Novembre, 10-13, 2011

  8. C. Cervellera; M. Cuneo; M. Gaggero; F. Tonelli

    "A decision support tool based on a queueing model for performance analysis and optimization of container terminals", 2010 Annual Conference of the International Association of Maritime Economists, Lisbon (Portugal), 7-9 July 2010

  9. C. Cervellera, M. Cuneo, M. Gaggero and F. Tonelli

    (2010) A model-based decision support tool for performance analysis and optimization of container terminals, in 2010 Annual Conference of the International Association of Maritime Economists, Lisbon, Portugal, Lisbon, Portugal

  10. C. Cervellera, D. Macciò and M. Muselli

    (2010)Semi-local approximation for the efficient solution of functional optimization problemsin INFORMS 2010 Annual Meeting, Austin, TX, U.S.A

  11. L. Caviglione, C. Cervellera

    (2009) An Optimized Architecture for Supporting Data Streaming in Interactive Grids, in 4th International Workshop on Distributed Cooperative Laboratories: Instrumenting the Grid, Alghero, Italy

  12. Luca Caviglione, Cristiano Cervellera

    (2009) Design and Performance Evaluation of a Content Distribution Overlay Optimized for Streaming, in Internat. Symp. on Performance Evaluation of Computer and Telecommun. Syst. (SPECTS’09), Istanbul, Turchia

  13. A. Alessandri, C. Cervellera, D. Macciò and M. Sanguineti

    (2009) Design of estimators via optimization based on quasi-Monte Carlo sampling, in AIRO Winter 2009, Cortina d'Ampezzo

BOOK CHAPTERS

  1. A. Alessandri; C. Cervellera; M. Cuneo; M. Gaggero

    (2009) Nonlinear model predictive control for resource allocation in the management of intermodal container terminals, in Nonlinear Model Predictive Control - Towards new challenging applications, Lecture notes in control and information sciences, Vol. 384, Springer