Machine Learning

Advancing frontiers of machine learning and data mining by developing novel methodologies and algorithmic solutions for 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 large 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 signal 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 Machine Intelligence department 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 focus on matrix and tensor factorization; and deep learning applications.

 

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

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2020

Proceedings, refereed

In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020.
Status: Accepted
In International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE'20), 2020.
Status: Accepted
In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020.
Status: Accepted
In The ACM Multimedia Systems Conference (MMSys). The ACM Multimedia Systems Conference (MMSys): ACM, 2020.
Status: Published
In SAIS Workshop 2020, the 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS). Chalmers: Chalmers.se SAIS 2020 online proceedings, 2020.
Status: Published

Book chapters

In Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier, 2020.
Status: Published
2019

Journal articles