Leibniz Universität Hannover Fakultät Fakultät für Elektrotechnik und Informatik
Kontakt
Institut für Künstliche Intelligenz
 
Institut für Künstliche Intelligenz LUHAI Institut
Tanja Tornede
Tanja Tornede
Telefon
+49 511 762 19749
E-Mail
t.tornede@ai.uni-hannover.de
Adresse
Welfengarten 1
30167 Hannover
Gebäude
1101
Raum
b404
Tanja Tornede
Telefon
+49 511 762 19749
E-Mail
t.tornede@ai.uni-hannover.de
Adresse
Welfengarten 1
30167 Hannover
Gebäude
1101
Raum
b404

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 - 9 von 9

2024

Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (2024). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. In Proceedings of the 38th conference on AAAI (S. 12172-12180). (Proceedings of the AAAI Conference on Artificial Intelligence; Band 38, Nr. 11). https://doi.org/10.48550/arXiv.2309.03581, https://doi.org/10.1609/aaai.v38i11.29106
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Hennig, L., Tornede, T., & Lindauer, M. (2024). Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2404.01965
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2023

Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2023). Algorithm selection on a meta level. Machine learning, 112(4), 1253-1286. Vorabveröffentlichung online. https://doi.org/10.1007/s10994-022-06161-4
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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. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2306.08107
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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
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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
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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
Mehr...
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
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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), Artikel 2099. https://doi.org/10.3390/s20072099
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Letzte Änderung: 24.11.23 Druckversion

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