2017 - Fellow of the International Federation of Automatic Control (IFAC)
2012 - IEEE Fellow For contributions to stochastic and randomized methods in systems and control
His primary areas of study are Mathematical optimization, Control theory, Scenario optimization, System identification and Control engineering. His research integrates issues of Randomized algorithm and Robustness in his study of Mathematical optimization. His study in the field of Stochastic control is also linked to topics like Colored.
He carries out multidisciplinary research, doing studies in Scenario optimization and Convex optimization. His System identification study combines topics in areas such as Algorithm, Linear system, Applied mathematics and Confidence interval. His work in the fields of Control theory overlaps with other areas such as Direct method.
Marco C. Campi mainly investigates Mathematical optimization, Control theory, System identification, Applied mathematics and Algorithm. His work on Scenario optimization as part of general Mathematical optimization research is frequently linked to Convex optimization, bridging the gap between disciplines. His research in Scenario optimization focuses on subjects like Optimization problem, which are connected to Stochastic programming.
His Control theory research focuses on Control engineering and how it relates to Control and Transfer function. His studies examine the connections between System identification and genetics, as well as such issues in Data point, with regards to Confidence region. His research in the fields of Recursive least squares filter and Covariance matrix overlaps with other disciplines such as Scheme.
The scientist’s investigation covers issues in Mathematical optimization, System identification, Scenario optimization, Robustness and Applied mathematics. His Mathematical optimization study incorporates themes from Sample and Realization. His System identification research is multidisciplinary, relying on both Algorithm and Data point.
Marco C. Campi has researched Scenario optimization in several fields, including Optimization problem, Multi-swarm optimization and Leverage. His studies in Robustness integrate themes in fields like Machine learning and Probably approximately correct learning. His work is dedicated to discovering how Applied mathematics, Strong consistency are connected with Instrumental variable and other disciplines.
Mathematical optimization, Scenario optimization, Robustness, Probabilistic-based design optimization and Stochastic programming are his primary areas of study. His Mathematical optimization study frequently involves adjacent topics like Realization. As part of one scientific family, Marco C. Campi deals mainly with the area of Scenario optimization, narrowing it down to issues related to the Multi-swarm optimization, and often Randomized algorithm and Fast algorithm.
His work focuses on many connections between Randomized algorithm and other disciplines, such as Sample, that overlap with his field of interest in System identification. His study in Robustness intersects with areas of studies such as Convex optimization, Software and Numerical analysis. His work deals with themes such as Robust optimization, Industrial engineering and Engineering optimization, which intersect with Probabilistic-based design optimization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The scenario approach to robust control design
G.C. Calafiore;M.C. Campi.
IEEE Transactions on Automatic Control (2006)
Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers
M. C. Campi;A. Lecchini;S. M. Savaresi.
Uncertain convex programs: randomized solutions and confidence levels
Giuseppe Carlo Calafiore;Marco C. Campi.
Mathematical Programming (2005)
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
M. C. Campi;S. Garatti.
Siam Journal on Optimization (2008)
The scenario approach for systems and control design
Marco C. Campi;Simone Garatti;Maria Prandini.
Annual Reviews in Control (2008)
A Sampling-and-Discarding Approach to Chance-Constrained Optimization: Feasibility and Optimality
Marco C. Campi;Simone Garatti.
Journal of Optimization Theory and Applications (2011)
Direct nonlinear control design: the virtual reference feedback tuning (VRFT) approach
M.C. Campi;S.M. Savaresi.
IEEE Transactions on Automatic Control (2006)
Virtual reference feedback tuning for two degree of freedom controllers
A. Lecchini;MC Campi;SM Savaresi.
International Journal of Adaptive Control and Signal Processing (2002)
Guaranteed non-asymptotic confidence regions in system identification
M. C. Campi;E. Weyer.
Convergence and exponential convergence of identification algorithms with directional forgetting factor
S. Bittanti;P. Bolzern;M. Campi.
Profile was last updated on December 6th, 2021.
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