Machine Learning

Advancing frontiers of machine learning and data mining by developing novel methodologies and algorithmic solutions for the analysis of complex systems and applying them to address challenging problems in high-impact applications.

Machine learning is one of the main enabling technologies today and fast becoming ubiquitous in various scientific and technological fields. Given a great demand for advanced machine learning methodologies and tools, the field of Machine Learning at Simula seeks to create and apply novel methods to provide new insights in a wide variety of applications ranging from biomedical signals and image analysis, systems biology to climate and communication networks, while contributing to the foundations of the scientific field.

At Simula Metropolitan Center for Digital Engineering, the focus of the department of Data Science and Knowledge Discovery is to advance frontiers of machine learning and data mining by developing novel methodologies and algorithmic solutions for the analysis of complex systems and high-dimensional data in science and industry. Our research activities span three general areas: statistical learning and regularization theory; data mining with a focus on the matrix and tensor factorization; and deep learning applications.

 

Simula's research activity on machine learning is based at SimulaMet. 

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2023

Journal articles

IEEE Transactions on Signal Processing (2023).
Status: Submitted
IEEE Signal Processing Letters (2023).
Status: Submitted
IEEE Transactions on Signal and Information Processing over Networks (2023).
Status: Accepted

Books

Proceedings, refereed

In MLSP'23: IEEE International Workshop on Machine Learning for Signal Processing. IEEE, 2023.
Status: Accepted
In International Conference on Multimedia Modeling (MMM). Vol. 13833. Cham: Springer International Publishing, 2023.
Status: Published
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.
Status: Accepted