ProfessorsProfessors of Uppsala UniversitetNiklas Wahlström

Niklas Wahlström

Uppsala Universitet

H-index: 19

Europe-Sweden

About Niklas Wahlström

Niklas Wahlström, With an exceptional h-index of 19 and a recent h-index of 15 (since 2020), a distinguished researcher at Uppsala Universitet, specializes in the field of Machine learning, deep learning, sensor fusion, state estimation, filtering.

His recent articles reflect a diverse array of research interests and contributions to the field:

Invertible Kernel PCA with Random Fourier Features

Incorporating Sum Constraints into Multitask Gaussian Processes

Physics-informed neural networks with unknown measurement noise

Machine learning: a first course for engineers and scientists

First steps towards self-supervised pretraining of the 12-lead ECG

Learning deep autoregressive models for hierarchical data

Deep state space models for nonlinear system identification

Niklas Wahlström Information

University	Uppsala Universitet
Position	Assistant Professor Sweden
Citations(all)	1633
Citations(since 2020)	1213
Cited By	815
hIndex(all)	19
hIndex(since 2020)	15
i10Index(all)	28
i10Index(since 2020)	19
Email	Access Email
University Profile Page	Uppsala Universitet
Google Scholar	View Google Scholar Profile

Niklas Wahlström Skills & Research Interests

Machine learning

deep learning

sensor fusion

state estimation

filtering

Top articles of Niklas Wahlström

Title	Journal	Author(s)	Publication Date
Invertible Kernel PCA with Random Fourier Features	IEEE Signal Processing Letters	Daniel Gedon Antônio H Ribeiro Niklas Wahlstörm Thomas B Schön	2023/5/11
Incorporating Sum Constraints into Multitask Gaussian Processes	Transactions on Machine Learning Research (TMLR)	Philipp Pilar Carl Jidling Thomas B Schön Niklas Wahlström	2022
Physics-informed neural networks with unknown measurement noise	arXiv preprint arXiv:2211.15498	Philipp Pilar Niklas Wahlström	2022/11/28
Machine learning: a first course for engineers and scientists		Andreas Lindholm Niklas Wahlström Fredrik Lindsten Thomas B Schön	2022/3/31
First steps towards self-supervised pretraining of the 12-lead ECG		Daniel Gedon Antônio H Ribeiro Niklas Wahlström Thomas B Schön	2021/9/13
Learning deep autoregressive models for hierarchical data		Carl R Andersson Niklas Wahlström Thomas B Schön	2021
Deep state space models for nonlinear system identification		Daniel Gedon Niklas Wahlström Thomas B Schön Lennart Ljung	2021