Institut
Tanja Tornede
Tanja Tornede
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Tanja Tornede
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

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

  • Working Experience

    2023 - Present
    Doctoral Researcher, Leibniz University Hannover

    2019 - 2023
    Doctoral Researcher, Paderborn University

  • Education

    2019 - Present
    Ph.D. Student (Dr. rer. nat) supervised by Prof. Dr. Eyke Hüllermeier, Paderborn University

    2015 - 2018
    Master of Science, Computer Science, Paderborn University

    2012 - 2015
    Bachelor of Science, Computer Science, Paderborn University

Publications

Zeige Ergebnisse 1 - 8 von 8

2024


Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (Angenommen/Im Druck). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. in Proceedings of the Thirty-Eighth Conference on Artificial Intelligence (AAAI'24)

2023


Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2023). Algorithm selection on a meta level. Machine learning, 112(4), 1253-1286. https://doi.org/10.1007/s10994-022-06161-4
Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2023). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. https://doi.org/10.48550/arXiv.2306.08107
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. https://doi.org/10.21105/joss.05149
Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & Hüllermeier, E. (2023). Towards Green Automated Machine Learning: Status Quo and Future Directions. Journal of Artificial Intelligence Research, 77, 427-457. https://doi.org/10.1613/jair.1.14340

2021


Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2021). AutoML for Predictive Maintenance: One Tool to RUL Them All. in J. Gama, S. Pashami, A. Bifet, M. Sayed-Mouchawe, H. Fröning, F. Pernkopf, G. Schiele, & M. Blott (Hrsg.), IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers (1 Aufl., S. 106–118). (Communications in Computer and Information Science; Band 1325). Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_8
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 (S. 368-376). (ACM Conferences). Association for Computing Machinery (ACM). https://doi.org/10.1145/3449639.3459395

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]. https://doi.org/10.3390/s20072099