Tomas Olsson

Tomas has been working as a researcher and software developer at RISE SICS since 1998. He has a long experience in both software development and in data analysis. His research interests are in applied statistical machine learning.

Degrees:
1998 M. Sc. Computer Science, KTH
2006 Ph.Lic. Computer Science, Uppsala University
2015 Ph.D. Computer Science, Mälardalen University

Selected publications:
Olsson, T., Ramentol, E., Rahman, M., Oostveen, M., & Kyprianidis, K. (2021). A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines. Energy and AI, 100064.

Ramentol, E., Olsson, T., & Barua, S. (2021). Machine Learning Models for Industrial Applications. In AI and Learning Systems-Industrial Applications and Future Directions. IntechOpen.

Källström, E., Olsson, T., Lindström, J., Håkansson, L., & Larsson, J. (2018). On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine. International Journal of Prognostics and Health Management, 9(1).

Emruli, B., Olsson, T., & Hoist, A. (2017). PyISC : A Bayesian anomaly detection framework for python. In FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (pp. 514–519).

Nessen, T, Nyfjord, J, Olsson, T, Andrén, D, Larsson, S, Wikström, A, Cedergren, S. (2017). Towards a predictive model for delivering product development projects on time. 24th Innovation and Product Development Management Conference, Reykjavik.

Olsson, T., Xiong, N., Källström, E., Holst, A., & Funk, P. (2015). Fault Diagnosis via Fusion of Information from a Case Stream. In International Conference on Case-Based Reasoning (pp. 275-289). Springer, Cham.

Olsson, T., Gillblad, D., Funk, P., & Xiong, N. (2014). Case-based reasoning for explaining probabilistic machine learning. International Journal of Computer Science and Information Technology, 6(2), 87-101.

See more:
https://scholar.google.se/citations?user=Xp3e7UEAAAAJ&hl=en