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Simo Särkkä

Simo Särkkä

D-Index & Metrics

Computer Science

D-Index
44
Citations
8739
World Ranking
7535
National Ranking
55

Overview

Simo Särkkä is a researcher affiliated with Aalto University in Finland, focusing on fields spanning Computer Science and Engineering. Their research contributions encompass a significant number of publications addressing topics in Artificial Intelligence, Control and Systems Engineering, and Radiology, Nuclear Medicine and Imaging.

The main topics covered by Särkkä's work include:

  • Target Tracking and Data Fusion in Sensor Networks
  • Gaussian Processes and Bayesian Inference
  • Control Systems and Identification
  • Fault Detection and Control Systems
  • Advanced Control Systems Optimization
  • Medical Imaging Techniques and Applications
  • Advanced X-ray and CT Imaging

Särkkä has authored several research papers published in well-known venues. Some notable publications include:

  • "Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review", 2020, IEEE Transactions on Intelligent Transportation Systems
  • "Early oxygen levels contribute to brain injury in extremely preterm infants", 2021, Pediatric Research
  • "Machine Learning Methods for Neonatal Mortality and Morbidity Classification", 2020, IEEE Access
  • "Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification", 2022, IEEE Access
  • "Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection", 2020, Journal of Signal Processing Systems

The frequent co-authors collaborating with Särkkä are:

  • Zheng Zhao
  • Adrien Corenflos
  • Ángel F. García-Fernández
  • Toni Karvonen
  • Muhammad F. Emzir

Regarding publication outlets, Särkkä has contributed extensively to venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Automatic Control
  • 2022 25th International Conference on Information Fusion (FUSION)
  • IEEE Access
  • IEEE Transactions on Signal Processing

In addition to journal articles, Särkkä has published a book with Cambridge University Press titled Bayesian Filtering and Smoothing (2023), which has been cited in the research community.

Best Publications

  • Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

    S. Sarkka;A. Nummenmaa

  • On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems

    S. Sarkka

  • Unscented Rauch--Tung--Striebel Smoother

    S. Sarkka

  • Rao-Blackwellized particle filter for multiple target tracking

    Simo Särkkä;Aki Vehtari;Jouko Lampinen

  • Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering

    S. Sarkka;A. Solin;J. Hartikainen

  • Kalman filtering and smoothing solutions to temporal Gaussian process regression models

    Jouni Hartikainen;Simo Sarkka

  • Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

    Sarang Thombre;Zheng Zhao;Henrik Ramm-Schmidt;José M. Vallet García

  • A survey of Monte Carlo methods for parameter estimation

    David Luengo;Luca Martino;Luca Martino;Mónica F. Bugallo;Victor Elvira

  • Hilbert space methods for reduced-rank Gaussian process regression

    Arno Solin;Simo Särkkä

  • Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution

    Robert Piche;Simo Sarkka;Jouni Hartikainen

  • Recursive Bayesian inference on stochastic differential equations

    Simo Särkkä

  • Applied Stochastic Differential Equations

    Simo Särkkä;Arno Solin

  • Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

    Simo Särkkä;Arno Solin;Aapo Nummenmaa;Aapo Nummenmaa;Aki Vehtari

  • Linear operators and stochastic partial differential equations in Gaussian process regression

    Simo Särkkä

  • Posterior Linearization Filter: Principles and Implementation Using Sigma Points

    Ángel F. García-Fernández;Lennart Svensson;Mark R. Morelande;Simo Särkkä

  • Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression

    Tim D. Barfoot;Chi Hay Tong;Simo Särkkä

  • Non-linear noise adaptive Kalman filtering via variational Bayes

    Simo Sarkka;Jouni Hartikainen

  • Gaussian filtering and smoothing for continuous-discrete dynamic systems

    Simo SäRkkä;Juha Sarmavuori

  • On Gaussian Optimal Smoothing of Non-Linear State Space Models

    Simo Sarkka;Jouni Hartikainen

  • Phase-Based UHF RFID Tracking With Nonlinear Kalman Filtering and Smoothing

    S. Sarkka;V. V. Viikari;M. Huusko;K. Jaakkola

  • Optimal Filtering with Kalman Filters and Smoothers

    Jouni Hartikainen;Arno Solin;Simo Särkkä

  • Bayesian Filtering and Smoothing: Bayesian filtering equations and exact solutions

    Simo Särkkä

Frequent Co-Authors

Aki Vehtari
Aki Vehtari Aalto University
Juho Kannala
Juho Kannala Aalto University
Janne Heikkilä
Janne Heikkilä University of Oulu
Jiri Matas
Jiri Matas Czech Technical University in Prague
Thomas B. Schön
Thomas B. Schön Uppsala University
Simon J. Godsill
Simon J. Godsill University of Cambridge
Philipp Hennig
Philipp Hennig University of Tübingen
Heikki Haario
Heikki Haario Lappeenranta University of Technology
Luca Martino
Luca Martino King Juan Carlos University
Neil D. Lawrence
Neil D. Lawrence University of Cambridge

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