Department of Data Science and Knowledge Discovery (DataSci) aims at advancing frontiers of machine learning and data mining by developing novel methodologies and algorithmic solutions for the analysis of high-dimensional data in science and industry.
Machine learning and data science have gained enormous international momentum, and it has been defined as a separate research topic at Simula since 2018. In particular, as a part of Simula Metropolitan Center for Digital Engineering, Data Science and Knowledge Discovery Department (DataSci) - previously known as the Machine Intelligence Department - was established in 2018.
DataSci at SimulaMet focuses on developing novel data mining/machine learning methods for the analysis of heterogeneous incomplete data (e.g., multi-modal, static, time-evolving, with missing entries) collected from complex systems (e.g., brain, human metabolome), with the goal of revealing interpretable patterns that can lead to improved understanding of such systems. Research activities at DataSci span the following areas: low-rank approximations, multimodal data mining (data fusion, coupled matrix/tensor factorizations), temporal data mining, numerical linear algebra, multilinear algebra, algorithms (numerical optimization) - with applications in precision medicine, phenotyping, omics data analysis, and neuroimaging data analysis (fMRI, EEG, dynamic brain connectivity, multimodal neuroimaging data analysis).