Tomas Olsson

Tomas har arbetat som forskare och mjukvaruutvecklare på RISE föregångare SICS sedan 1998. Han har lång erfarenhet av både mjukvaruutveckling och dataanalys. Hans forskningsintressen är tillämpad statistisk maskininlärning. Examen: 1998 M. Sc. Computer Science, KTH 2006 Ph.Lic. Computer Science, Uppsala University 2015 Ph.D. Computer Science, Mälardalen University Utvalda publikationer: 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. Se mer: https://scholar.google.se/citations?user=1RGAtbEAAAAJ