What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
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.
His most cited work include:
- The scenario approach to robust control design (777 citations)
- Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers (668 citations)
- Uncertain convex programs: randomized solutions and confidence levels (493 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Mathematical optimization (45.02%)
- Control theory (27.96%)
- System identification (27.96%)
What were the highlights of his more recent work (between 2013-2021)?
- Mathematical optimization (45.02%)
- System identification (27.96%)
- Scenario optimization (10.43%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- Wait-and-judge scenario optimization (68 citations)
- A General Scenario Theory for Nonconvex Optimization and Decision Making (51 citations)
- Advanced optimization methods for power systems (44 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
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.
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