30167 Hannover
Research Interests
My research interest are driven by the desire to create a more sustainable and worth living world for everyone. Therefore, i am interested in research on Green AutoML, which focuses on developing environmentally friendly and energy-efficient AutoML. Furthermore, I am interested in AutoML for Predictive Maintenance, which enables industrial domain experts to apply machine learning, e.g. for Remaining Useful Lifetime estimation, without advanced knowledge of machine learning.
Curriculum Vitae
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Working Experience
2023 - Present
Doctoral Researcher, Leibniz University Hannover2019 - 2023
Doctoral Researcher, Paderborn University -
Education
2019 - Present
Ph.D. Student (Dr. rer. nat) supervised by Prof. Dr. Eyke Hüllermeier, Paderborn University2015 - 2018
Master of Science, Computer Science, Paderborn University2012 - 2015
Bachelor of Science, Computer Science, Paderborn University
Publications
2023
Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2023). Algorithm selection on a meta level. Machine learning, 112(4), 1253-1286.
Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F., & Wever, M. (2023). PyExperimenter: Easily distribute experiments and track results. Journal of Open Source Software.
2021
Tornede, T., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In Proceedings of the Genetic and Evolutionary Computation Conference
Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
2020
Hoffmann, M. W., Wildermuth, S., Gitzel, R., Boyaci, A., Gebhardt, J., Kaul, H., Amihai, I., Forg, B., Suriyah, M., Leibfried, T., Stich, V., Hicking, J., Bremer, M., Kaminski, L., Beverungen, D., Heiden, P. Z., & Tornede, T. (2020). Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions. Sensors, 20(7), [2099].
2019
Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2019). AutoML for Predictive Maintenance: One Tool to RUL them all. In IoT Streams 2020: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning
