Niklas Wahlström
Senior Lecturer/Associate Professor at Department of Information Technology; Division of Systems and Control
- Telephone:
- +46 18 471 31 89
- E-mail:
- niklas.wahlstrom@it.uu.se
- Visiting address:
- Hus 10, Regementsvägen 10
- Postal address:
- Box 337
751 05 UPPSALA
- Academic merits:
- Docent in machine learning
- CV:
- Download CV
Short presentation
I am an Associate Professor at the Division of Systems and Control, Department of Information Technology, Uppsala University. My research interests lie in physics-informed machine learning and applications of machine learning in physics.
My teaching
My research
My publications
Keywords
- artificial intelligence
- machine learning
- automatic control
- deep learning
- signal processing
- sensor fusion
Biography
Niklas Wahlström is an Associate Professor at the Division of Systems and Control, Department of Information Technology, Uppsala University. His research interests lie in the fields of machine learning, sensor fusion, and statistical signal processing, together with their applications. He is especially interested in physics-informed machine learning and applications of machine learning in physics. He has developed several courses in machine learning, both at Master's and PhD level. Niklas received his MSc degree in 2010 and his PhD degree in automatic control in 2015, both from Linköping University, Sweden. During his studies, he held visiting positions at ETH Zürich (Switzerland) and Imperial College (UK). Since 2016, he has been affiliated with Uppsala University, first as a postdoctoral researcher, and since 2019 in his present position.

Publications
Recent publications
Physics-informed neural networks with unknown measurement noise
Part of Proceedings of Machine Learning Research, p. 235-247, 2024
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
Part of Transactions on Machine Learning Research, 2024
Invertible Kernel PCA With Random Fourier Features
Part of IEEE Signal Processing Letters, p. 563-567, 2023
Machine learning: a first course for engineers and scientists
Cambridge University Press, 2022
Learning deep autoregressive models for hierarchical data
Part of IFAC PapersOnLine, p. 529-534, 2021
- DOI for Learning deep autoregressive models for hierarchical data
- Download full text (pdf) of Learning deep autoregressive models for hierarchical data
All publications
Articles in journal
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
Part of Transactions on Machine Learning Research, 2024
Invertible Kernel PCA With Random Fourier Features
Part of IEEE Signal Processing Letters, p. 563-567, 2023
Probabilistic approach to limited-data computed tomography reconstruction
Part of Inverse Problems, 2019
Probabilistic modelling and reconstruction of strain
Part of Nuclear Instruments and Methods in Physics Research Section B, p. 141-155, 2018
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes
Part of IEEE Transactions on robotics, p. 1112-1127, 2018
A Platform for Teaching Sensor Fusion Using a Smartphone
Part of International journal of engineering education, p. 781-789, 2017
Books
Machine learning: a first course for engineers and scientists
Cambridge University Press, 2022
Conference papers
Physics-informed neural networks with unknown measurement noise
Part of Proceedings of Machine Learning Research, p. 235-247, 2024
Learning deep autoregressive models for hierarchical data
Part of IFAC PapersOnLine, p. 529-534, 2021
- DOI for Learning deep autoregressive models for hierarchical data
- Download full text (pdf) of Learning deep autoregressive models for hierarchical data
First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG
Part of 2021 Computing In Cardiology (CINC), 2021
Deep State Space Models for Nonlinear System Identification
Part of IFAC PapersOnLine, p. 481-486, 2021
- DOI for Deep State Space Models for Nonlinear System Identification
- Download full text (pdf) of Deep State Space Models for Nonlinear System Identification
Deep convolutional networks in system identification
Part of Proc. 58th IEEE Conference on Decision and Control, p. 3670-3676, 2019
Data-driven impulse response regularization via deep learning
p. 1-6, 2018
Linearly constrained Gaussian processes
Part of Proc. 31st Conference on Neural Information Processing Systems, p. 1215-1224, 2017
Rao-Blackwellised Particle Filter for Star-ConvexExtended Target Tracking Models
Part of 2016 19th International Conference on Information Fusion, p. 1193-1199, 2016