Niklas Wahlström
Universitetslektor i reglerteknik vid Institutionen för informationsteknologi; Systemteknik
- Telefon:
- 018-471 31 89
- E-post:
- niklas.wahlstrom@it.uu.se
- Besöksadress:
- Hus 10, Regementsvägen 10
- Postadress:
- Box 524
751 20 UPPSALA
- Akademiska meriter:
- Docent i maskininlärning
- CV:
- Ladda ned CV
Kort presentation
Jag är universitetslektor på avdelningen för systemteknik vid Institutionen för Informationsteknologi vid Uppsala universitet. Mitt forskningsintresse ligger i fysikinformerad maskininlärning och tillämpningar av maskininlärning inom fysik.
Se min personliga hemsida för mer information.
Nyckelord
- artificial intelligence
- machine learning
- automatic control
- deep learning
- signal processing
- sensor fusion
Biografi
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 MSc level and at PhD level. Niklas received his MSc degree in 2010 and his PhD in automatic control in 2015, both from Linköping University, Sweden. He also did parts of his studies at ETH Zürich (Switzerland) and Imperial College (UK). Since 2016, he has been affiliated with Uppsala University, first as a Postdoctoral researcher, since 2019 as an Assistant Professor, and since 2019 in his present position.
Publikationer
Senaste publikationer
-
Ingår i Machine Learning, 2026
- DOI för A differentiable surrogate model for the generation of radio pulses from in-ice neutrino interactions
- Ladda ner fulltext (pdf) av A differentiable surrogate model for the generation of radio pulses from in-ice neutrino interactions
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Learning dynamics from input-output data with Hamiltonian Gaussian processes.
2026
-
Optimization pipeline for in-ice radio neutrino detectors
Ingår i Proceedings of Fifth MODE Workshop on Differentiable Programming for Experiment Design PoS(MODE2025), 2025
-
Physics-informed neural networks with unknown measurement noise
Ingår i Proceedings of Machine Learning Research, s. 235-247, 2024
-
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
Ingår i Transactions on Machine Learning Research, 2024
Alla publikationer
Artiklar i tidskrift
-
Ingår i Machine Learning, 2026
- DOI för A differentiable surrogate model for the generation of radio pulses from in-ice neutrino interactions
- Ladda ner fulltext (pdf) av A differentiable surrogate model for the generation of radio pulses from in-ice neutrino interactions
-
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
Ingår i Transactions on Machine Learning Research, 2024
-
Invertible Kernel PCA With Random Fourier Features
Ingår i IEEE Signal Processing Letters, s. 563-567, 2023
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Incorporating Sum Constraints into Multitask Gaussian Processes
Ingår i Transactions on Machine Learning Research, s. 1-28, 2022
-
Probabilistic approach to limited-data computed tomography reconstruction
Ingår i Inverse Problems, 2019
-
Probabilistic modelling and reconstruction of strain
Ingår i Nuclear Instruments and Methods in Physics Research Section B, s. 141-155, 2018
-
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes
Ingår i IEEE Transactions on robotics, s. 1112-1127, 2018
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A Platform for Teaching Sensor Fusion Using a Smartphone
Ingår i International journal of engineering education, s. 781-789, 2017
Böcker
-
Machine learning: a first course for engineers and scientists
Cambridge University Press, 2022
Konferensbidrag
-
Learning dynamics from input-output data with Hamiltonian Gaussian processes.
2026
-
Optimization pipeline for in-ice radio neutrino detectors
Ingår i Proceedings of Fifth MODE Workshop on Differentiable Programming for Experiment Design PoS(MODE2025), 2025
-
Physics-informed neural networks with unknown measurement noise
Ingår i Proceedings of Machine Learning Research, s. 235-247, 2024
-
Learning deep autoregressive models for hierarchical data
Ingår i IFAC PapersOnLine, s. 529-534, 2021
- DOI för Learning deep autoregressive models for hierarchical data
- Ladda ner fulltext (pdf) av Learning deep autoregressive models for hierarchical data
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First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG
Ingår i 2021 Computing In Cardiology (CINC), 2021
-
Deep State Space Models for Nonlinear System Identification
Ingår i IFAC PapersOnLine, s. 481-486, 2021
- DOI för Deep State Space Models for Nonlinear System Identification
- Ladda ner fulltext (pdf) av Deep State Space Models for Nonlinear System Identification
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Deep convolutional networks in system identification
Ingår i Proc. 58th IEEE Conference on Decision and Control, s. 3670-3676, 2019
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Data-driven impulse response regularization via deep learning
s. 1-6, 2018
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Linearly constrained Gaussian processes
Ingår i Proc. 31st Conference on Neural Information Processing Systems, s. 1215-1224, 2017
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Rao-Blackwellised Particle Filter for Star-ConvexExtended Target Tracking Models
Ingår i 2016 19th International Conference on Information Fusion, s. 1193-1199, 2016