What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mathematical analysis
Simo Särkkä mainly focuses on Kalman filter, Mathematical optimization, Applied mathematics, Smoothing and Extended Kalman filter.
His studies deal with areas such as Algorithm, Bayesian inference, Filter and Nonlinear system as well as Kalman filter.
His Algorithm study incorporates themes from Recursive Bayesian estimation, Particle filter and Hybrid Monte Carlo, Markov chain Monte Carlo.
His specific area of interest is Applied mathematics, where Simo Särkkä studies Stochastic differential equation.
The concepts of his Smoothing study are interwoven with issues in State space, Gaussian process and Trajectory.
His work deals with themes such as Kriging, Series expansion, Artificial intelligence, Covariance function and Approximation theory, which intersect with Gaussian process.
His most cited work include:
- On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems (375 citations)
- Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations (278 citations)
- Unscented Rauch--Tung--Striebel Smoother (222 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Applied mathematics, Kalman filter, Algorithm, Gaussian process and Smoothing.
Simo Särkkä combines subjects such as Covariance, Gaussian, Mathematical optimization and Nonlinear system with his study of Applied mathematics.
His Mathematical optimization research is multidisciplinary, relying on both Particle filter and State space.
His research in Kalman filter is mostly concerned with Extended Kalman filter.
His Algorithm study which covers Bayesian probability that intersects with Quadrature.
Simo Särkkä focuses mostly in the field of Gaussian process, narrowing it down to matters related to Covariance function and, in some cases, Mathematical analysis.
He most often published in these fields:
- Applied mathematics (39.69%)
- Kalman filter (26.72%)
- Algorithm (25.95%)
What were the highlights of his more recent work (between 2019-2021)?
- Applied mathematics (39.69%)
- Smoothing (21.37%)
- Algorithm (25.95%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Applied mathematics, Smoothing, Algorithm, Kalman filter and Gaussian process.
His research in Applied mathematics is mostly focused on Stochastic differential equation.
Simo Särkkä has researched Smoothing in several fields, including Discrete time and continuous time, Computation, Taylor series and Parallel computing.
His work in Algorithm addresses issues such as Graphics processing unit, which are connected to fields such as Time complexity and Logarithm.
His Kalman filter research integrates issues from Filter, Representation, State space, Noise and Nonlinear system.
Simo Särkkä works mostly in the field of Gaussian process, limiting it down to topics relating to Kriging and, in certain cases, Computational complexity theory, as a part of the same area of interest.
Between 2019 and 2021, his most popular works were:
- Hilbert space methods for reduced-rank Gaussian process regression (32 citations)
- Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions (6 citations)
- A survey of Monte Carlo methods for parameter estimation (5 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mathematical analysis
Simo Särkkä mostly deals with Applied mathematics, Kalman filter, Algorithm, Smoothing and Gaussian process.
His Applied mathematics research incorporates elements of Truncation, Orthonormal basis, Simple function, Prior probability and Kernel.
Simo Särkkä is interested in Kalman smoother, which is a branch of Kalman filter.
The various areas that Simo Särkkä examines in his Algorithm study include Mixture model, Position and Noise.
His Smoothing study combines topics from a wide range of disciplines, such as Stochastic differential equation, Gaussian, Bayesian probability and Regression.
His research in Gaussian process intersects with topics in Numerical integration, Probabilistic logic, Quadrature and Extended Kalman filter.
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.
