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- Giuseppe Carlo Calafiore

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D-index
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Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
32
Citations
8,028
162
World Ranking
4858
National Ranking
159

2018 - IEEE Fellow For contributions to probabilistic methods for robust control design

- Statistics
- Artificial intelligence
- Quantum mechanics

His scientific interests lie mostly in Mathematical optimization, Convex optimization, Probabilistic logic, Robust control and Randomized algorithm. Mathematical optimization and Robustness are commonly linked in his work. His Robustness research also works with subjects such as

- Computational complexity theory that connect with fields like Nonlinear system,
- Model predictive control that connect with fields like Optimal control.

His Convex optimization study combines topics in areas such as Discrete mathematics, Time complexity, Linear matrix inequality and Portfolio optimization, Portfolio. His study in Probabilistic logic is interdisciplinary in nature, drawing from both Measure, Oracle and Regular polygon. His Randomized algorithm study also includes fields such as

- Probabilistic analysis of algorithms, which have a strong connection to Machine learning, PageRank, Control theory and Stability,
- Control and Design methods most often made with reference to Probabilistic method.

- The scenario approach to robust control design (777 citations)
- Randomized Algorithms for Analysis and Control of Uncertain Systems (495 citations)
- Uncertain convex programs: randomized solutions and confidence levels (493 citations)

His primary areas of study are Mathematical optimization, Convex optimization, Probabilistic logic, Robustness and Control theory. Giuseppe Carlo Calafiore works mostly in the field of Mathematical optimization, limiting it down to concerns involving Model predictive control and, occasionally, Optimal control. His Convex optimization study integrates concerns from other disciplines, such as Posynomial, Geometric programming, Linear matrix inequality and Upper and lower bounds.

In his study, which falls under the umbrella issue of Probabilistic logic, Expected value is strongly linked to Randomized algorithm. Giuseppe Carlo Calafiore has included themes like Control system and Bounded function in his Robustness study. As part of his studies on Control theory, he frequently links adjacent subjects like Estimation theory.

- Mathematical optimization (44.57%)
- Convex optimization (18.48%)
- Probabilistic logic (15.58%)

- Mathematical optimization (44.57%)
- Applied mathematics (10.51%)
- Econometrics (4.35%)

Giuseppe Carlo Calafiore mainly investigates Mathematical optimization, Applied mathematics, Econometrics, Nonlinear system and Pattern recognition. His work carried out in the field of Mathematical optimization brings together such families of science as Differential inequalities, Social dynamics, Autonomous agent, Robustness and Laplace operator. His study looks at the relationship between Applied mathematics and fields such as Artificial neural network, as well as how they intersect with chemical problems.

His Nonlinear system study is concerned with the larger field of Control theory. His Function research includes themes of Model predictive control and Convex optimization. His study focuses on the intersection of Convex optimization and fields such as Log-log plot with connections in the field of Logarithm and Exponential function.

- A Modified SIR Model for the COVID-19 Contagion in Italy (50 citations)
- Log-Sum-Exp Neural Networks and Posynomial Models for Convex and Log-Log-Convex Data (24 citations)
- A Time-Varying SIRD Model for the COVID-19 Contagion in Italy. (23 citations)

- Statistics
- Artificial intelligence
- Quantum mechanics

His primary scientific interests are in Parameter identification problem, Coronavirus disease 2019, Econometrics, Optimization problem and Applied mathematics. His studies in Parameter identification problem integrate themes in fields like Nonlinear system identification, Model predictive control, Sobolev space, Function and Function approximation. Giuseppe Carlo Calafiore has included themes like Artificial neural network, Activation function, Feed forward, Continuous function and Numerical analysis in his Applied mathematics study.

His work in Hyperparameter optimization addresses issues such as Linear combination, which are connected to fields such as Mathematical optimization. Giuseppe Carlo Calafiore interconnects Benchmark, Graph, Focus, Symmetric matrix and Robustness in the investigation of issues within Mathematical optimization. He combines subjects such as Discrete mathematics, Posynomial and Convex optimization with his study of Logarithm.

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)**

1000 Citations

Randomized Algorithms for Analysis and Control of Uncertain Systems

Roberto Tempo;Giuseppe Calafiore;Fabrizio Dabbene.

**(2004)**

772 Citations

Uncertain convex programs: randomized solutions and confidence levels

Giuseppe Carlo Calafiore;Marco C. Campi.

Mathematical Programming **(2005)**

766 Citations

On Distributionally Robust Chance-Constrained Linear Programs

G. C. Calafiore;L. El Ghaoui.

Journal of Optimization Theory and Applications **(2006)**

406 Citations

Random Convex Programs

Giuseppe Carlo Calafiore.

Siam Journal on Optimization **(2010)**

259 Citations

Robust filtering for discrete-time systems with bounded noise and parametric uncertainty

L. El Ghaoui;G. Calafiore.

IEEE Transactions on Automatic Control **(2001)**

254 Citations

Survey paper: Research on probabilistic methods for control system design

Giuseppe C. Calafiore;Fabrizio Dabbene;Roberto Tempo.

Automatica **(2011)**

220 Citations

Robust Model Predictive Control via Scenario Optimization

Giuseppe C. Calafiore;L. Fagiano.

IEEE Transactions on Automatic Control **(2013)**

201 Citations

Randomized algorithms for probabilistic robustness with real and complex structured uncertainty

G.C. Calafiore;F. Dabbene;R. Tempo.

IEEE Transactions on Automatic Control **(2000)**

194 Citations

Stochastic algorithms for exact and approximate feasibility of robust LMIs

G. Calafiore;B.T. Polyak.

IEEE Transactions on Automatic Control **(2001)**

184 Citations

Polytechnic University of Turin

MIT

University of Brescia

University of California, Berkeley

Lawrence Berkeley National Laboratory

Politecnico di Milano

Georgia Institute of Technology

University of California, Santa Barbara

École Polytechnique

The University of Texas at Austin

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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