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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2022

Journal articles

Computers in Biology and Medicine 14312136320119704317507593739403621582 (2022).
Status: Published
IEEE Signal Processing Letters (2022).
Status: Submitted
Nordic Machine Intelligence 2 (2022): 1-3.
Status: Published
SIAM Journal on Mathematics of Data Science (2022).
Status: Accepted