What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary scientific interests are in Algorithm, System identification, Control theory, Mathematical optimization and Linear system.
His Algorithm study incorporates themes from Covariance, Kernel, Statistics and Artificial intelligence.
His System identification study integrates concerns from other disciplines, such as Minimum description length, Kernel method and Special case.
His research in Control theory intersects with topics in Estimation theory and Subspace topology.
The various areas that Alessandro Chiuso examines in his Mathematical optimization study include Marginal likelihood, Kalman filter and Dynamical system.
His studies deal with areas such as Smoothing, Observability, Hybrid system and Rank as well as Linear system.
His most cited work include:
- Dynamic Textures (820 citations)
- Distributed Kalman filtering based on consensus strategies (413 citations)
- Structure from motion causally integrated over time (299 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Algorithm, Mathematical optimization, System identification, Artificial intelligence and Applied mathematics.
His Algorithm study combines topics from a wide range of disciplines, such as Linear system, Monte Carlo method and White noise.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Marginal likelihood, Bayes estimator, Estimator, Kernel and Hyperparameter.
His studies in System identification integrate themes in fields like Regularization, Nonparametric statistics and Bayesian probability.
Alessandro Chiuso combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
While the research belongs to areas of Applied mathematics, he spends his time largely on the problem of Subspace topology, intersecting his research to questions surrounding Delta method, Control theory, Realization, Statistics and Closed loop.
He most often published in these fields:
- Algorithm (30.34%)
- Mathematical optimization (26.40%)
- System identification (25.28%)
What were the highlights of his more recent work (between 2015-2021)?
- Regularization (14.04%)
- Algorithm (30.34%)
- Bayesian probability (12.92%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Regularization, Algorithm, Bayesian probability, System identification and Artificial intelligence.
The study incorporates disciplines such as Control theory and Applied mathematics in addition to Regularization.
Alessandro Chiuso conducted interdisciplinary study in his works that combined Algorithm and Kernel.
Alessandro Chiuso has researched Bayesian probability in several fields, including Probability distribution, Mathematical optimization and Impulse response.
His System identification research incorporates themes from Marginal likelihood and Bayesian inference.
As a part of the same scientific study, Alessandro Chiuso usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Noisy data and Perspective.
Between 2015 and 2021, his most popular works were:
- Sparse plus Low rank Network Identification: A Nonparametric Approach (42 citations)
- Regularized linear system identification using atomic, nuclear and kernel-based norms (28 citations)
- Regularization and Bayesian learning in dynamical systems: Past, present and future☆ (27 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Alessandro Chiuso mainly focuses on Gaussian process, Regularization, Kernel method, Applied mathematics and Inverse problem.
Throughout his Gaussian process studies, Alessandro Chiuso incorporates elements of other sciences such as Spline, Estimator, Noisy data, System identification and Machine learning.
His study explores the link between Regularization and topics such as Bayesian probability that cross with problems in Management science, Automatic control, Parametric statistics and Linear system.
His work carried out in the field of Inverse problem brings together such families of science as Inverse dynamics, iCub, Robotics and Artificial intelligence.
Kernel is frequently linked to Mathematical optimization in his study.
His Mathematical optimization research is multidisciplinary, incorporating elements of Linear system identification and Impulse response.
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