Research at Systems and Control

Modeling, inference and control in complex dynamical systems

Current projects

Automatic control and System identification

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 Medvedev

Statistical Machine Learning

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 Schön

Signal Processing

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 Schön

Important application areas

Biomedical systems

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 Medvedev

Wastewater engineering

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 chemicals consumption and yield energy savings. Contact person: Bengt Carlsson

Recent publications

More comprehensive list of publications

CellMCD plus: An improved outlier-resistant cellwise minimum covariance determinant method. Prabhu Babu and Peter Stoica. In Statistics and Probability Letters, volume 220, Elsevier, 2025. (DOI).
Penalized Likelihood Approach for Graph Learning in the Presence of Outliers. Ghania Fatima, Peter Stoica, and Prabhu Babu. In IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, volume 11, pp 187-200, Institute of Electrical and Electronics Engineers (IEEE), 2025. (DOI).
Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study. Arunashis Sau, Ewa Sieliwonczyk, Konstantinos Patlatzoglou, Libor Pastika, Kathryn A McGurk, Antônio Horta Ribeiro, Antonio Luiz P Ribeiro, Jennifer E Ho, Nicholas S Peters, James S Ware, Upasana Tayal, Daniel B Kramer, Jonathan W Waks, and Fu Siong Ng. In The Lancet Digital Health, volume 7, number 3, pp e184-e194, 2025. (DOI).
Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study. Philip Hempel, Antônio Horta Ribeiro, Marcus Vollmer, Theresa Bender, Marcus Dörr, Dagmar Krefting, and Nicolai Spicher. In npj Digital Medicine, volume 8, number 1, Springer Nature, 2025. (DOI, Fulltext, fulltext:print).
l0 Penalized Maximum Likelihood Estimation of Sparse Covariance Matrices. Ghania Fatima, Peter Stoica, and Prabhu Babu. In IEEE Signal Processing Letters, volume 32, pp 66-70, Institute of Electrical and Electronics Engineers (IEEE), 2025. (DOI).
Rättsliga hinder mot samverkan?: Juridik och ledningsfrågor vid doktorandsamverkan. Magnus Strand, Linda Wedlin, and Thomas B. Schön. SNS förlag, Stockholm, 2025.
Deep networks for system identification: A survey. Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon, Lennart Ljung, Antônio H. Ribeiro, and Thomas B. Schön. In Automatica, volume 171, Elsevier, 2025. (DOI, Fulltext, fulltext:print).
Search for B+c ? ?+?+?- decays and measurement of the branching fraction ratio B(B+c ? ?(2S)?+)/B(B+c ? J/??+). R. Aaij, Patrik Adlarson, Alexander Medvedev, D. Bobulska, N. Cooke, A. D. Docheva, L. Douglas, Lars Eklund, L. Grillo, S. J. Jaimes Elles, Andrzej Kupsc, N. T. McHugh, H. N. Nur, G. Robertson, J. Ruiz Vidal, M. Schiller, F. J. P. Soler, P. Spradlin, M. Whitehead, and G. Zunica. In European Physical Journal C, volume 84, number 5, SPRINGER, 2024. (DOI, Fulltext, fulltext:print).
Soft sensor for the dry solid content in thickened primary sludge. Hanna Molin, Eric Bröndum, Sara Nilsson, Per Mattsson, Ramesh Saagi, Erik Lindblom, Bengt Carlsson, and Ulf Jeppsson. In Water Science and Technology, volume 90, number 7, pp 1946-1956, IWA Publishing, 2024. (DOI, Fulltext, fulltext:print).
Prediction of on-load tap-changer switch time from vibroacoustic measurements by machine learnings. Joachim Schiessling, Per Mattsson, Erik Victorin, Cecilia Forssén, and Nilanga Abeywickrama. In , pp 350-354, 2024. (DOI).

Updated 2022-09-07 14:28:08 by Victor Kuismin.