My research focuses on analysis of multivariate data in general, with a strong emphasis on analysis of healthcare data, consumer and sensory data as well as data from the agricultural sciences. This includes research and development of multivariate statistical methods, such as multiblock methods that are suitable for analysis of multivariate data from different measurement sources with varying complexity, as well as application of machine learning and deep learning methods in the above mentioned areas. Further aspects of my research are the development of software packages and user-friendly software for analysis of multivariate data. Another important part of my work is teaching courses in programming and applied machine learning and supervision of PhD and master students.
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Publications
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Projects & research
Projects
Using big data to select the best catalytic materials to transform fish waste into valuable chemicals.
The expected outcome of this project is to develop a methodology for prediction and trading of power flexibility, including the development of new business models for future flexibility markets.
Thermoelectric materials can turn heat into electricity, but better materials are needed for large-scale recovery of waste heat. Our project aims to identify materials with great promise using only computer experiments and machine learning.
Areas of research