Research at Systems and Control
Modeling, inference and control in complex dynamical systems
Current projects
Dynamical systems, physical and others, can be described by a variety of mathematical models. We develop methods for modeling and estimation of dynamical systems based on measurements of input/output data. The models can be used for analysis, in order to better understand the properties of the system, or for control, to automatically regulate a process without human interaction. Contact person: Alexander MedvedevAutomatic control and System identification
Many interesting phenomena around us are complex, dynamical and stochastic in nature, and the available data are inherently uncertain. We develop theory and tools for learning, reasoning and acting based on probabilistic models and measured data, methods that allow humans and machines to better understand the surrounding world. Contact person: Thomas SchönStatistical Machine Learning
The goal of signal processing is to extract information from measured quantities, or signals. This broad concept ranges from simple linear filtering of time series to reduce noise, to nonlinear parameter estimation based on high-dimensional data using statistical models. Estimation theory, optimization, and statistics, play central roles. Contact person: Thomas SchönSignal Processing
Important application areas
The theory and methods of dynamical systems, control, identification and signal processing have much to offer research and clinical practice in modern medicine. We develop methods used in the diagnosis, assessment, and treatment of medical conditions, based on dynamical models of physiological and biological systems. Currently we have projects on Parkinson's disease, breast cancer, diabetes and balance impairment. Contact person: Alexander MedvedevBiomedical systems
Water quality and treatment of water is a growing concern around the world. Demands on quality and increasing loads call for optimized operation of wastewater treatment plants. Applied research in automatic control is an important tool in improving the performance of treatment plants. We are developing control and estimation strategies for wastewater treatment plants that improve pollutant removal, reduce the need for chemicals and yield energy savings. Contact person: Bengt CarlssonWastewater engineering
Recent publications
More comprehensive list of publications
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Fluoxetine and Fractures After Stroke.
. In Stroke, 2019. (DOI). Publication status: Epub ahead of print
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Tailoring Gaussian processes for tomographic reconstruction. Licentiate thesis, IT licentiate theses / Uppsala University, Department of Information Technology nr 2019-005, Uppsala University, 2019. (fulltext
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Max-Min Fairness Design for MIMO Interference Channels: A Minorization-Maximization Approach. In IEEE Transactions on Signal Processing, volume 67, number 18, pp 4707-4719, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019. (DOI).
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Probabilistic approach to limited-data computed tomography reconstruction. In Inverse Problems, volume 35, number 10, 2019. (DOI).
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Deep kernel learning for integral measurements. In Computing Research Repository, number 1909.01844, 2019. (External link). Publication status: Submitted
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Attractivity of the synchronous mode in hybrid observers for the impulsive Goodwin's oscillator subject to harmonic exogenous excitation. In Proc. American Control Conference: ACC 2019, pp 2334-2339, American Automatic Control Council, 2019.
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High-Dimensional Filtering Using Nested Sequential Monte Carlo. In IEEE Transactions on Signal Processing, volume 67, number 16, pp 4177-4188, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019. (DOI).
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Effect Inference From Two-Group Data With Sampling Bias. In IEEE Signal Processing Letters, volume 26, number 8, pp 1103-1106, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019. (DOI).
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Machine learning techniques for monitoring the sludge profile in a secondary settler tank. In Applied water science, volume 9, number 6, 2019. (DOI, Fulltext).
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Neutron transmission strain tomography for non-constant stress-free lattice spacing. In Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, volume 456, pp 64-73, 2019. (DOI).