30167 Hannover
My Vision
Artificial intelligence is no longer science fiction, but can be found all around us. Be it the personal assistant on your smartphone, the driving assistant of your car, or the setup of your favorite clothing store - chances are that all of these leverage AI and/or have been created with the help of AI. As such, it might seem that AI is democratized and more or less easily available as a supporting tool for researchers and businesses. However, the majority of AI solutions present in our daily life has been developed by and are only available to large companies due to the associated personnel costs of employing data scientists.
Considering the impressive advances AI achieved in the recent years in fields such as natural language processing, image processing and product recommendation, this is unfortunate. Instead, I believe that everyone should be enabled to leverage these approaches to improve the world we live in. Therefore, my goal is to democratize AI and in particular machine learning (ML) by research in the area of automated machine learning (AutoML). I aim at enabling researchers from other disciplines and companies, which might not be able to afford large data science teams, to leverage ML through the means of AutoML tools.
To achieve this, I research on interactive and explainable AutoML targeted at supporting data scientists and domain experts in leveraging ML for their particular application in an intuitive, human-centered and explainable way. Together with my awesome colleagues, we work hard to make existing AutoML tools easier to use and understand by non-experts and in particular improve the understandability of the underlying AutoML process. We hope that this will demystify AutoML, such that non-expert users can interact with AutoML tools by injecting domain knowledge and tailoring the search process to their needs.
Research Interests
My interests are centered around different topics in machine learning and artificial intelligence, and in particular the automation thereof, called automated machine learning (AutoML). I am most interested in the following topics:
- Interactive and Explainable AutoML
- Green AutoML
- Algorithm Selection
- Applications to health/medicine
Curriculum Vitae
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Working Experience
2022 - Present
Doctoral Researcher, Leibniz University Hannover2018 - 2022
Doctoral Researcher, Paderborn University -
Education
2018 - 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 -
Selected Awards
2020
Frontier Prize at the Symposium on Intelligent Data Analysis (Online) - jointly with Marcel Wever, Felix Mohr and Eyke Hüllermeier2019
Young Author Award at the 29th Workshop on Computational Intelligence (Dortmund, Germany) -
Memberships
2022 - Present
Co-General Chair of COSEAL network2021 - 2022
Review Workflow Chair at the First International Conference on Automated Machine Learning - Social Media
Publications
2023
Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F., & Wever, M. (2023). PyExperimenter: Easily distribute experiments and track results.
2022
Fehring, L., Hanselle, J., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In NeurIPS Workshop on Meta Learning (MetaLearn 2022)
Gevers, K., Tornede, A., Wever, M., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the world, 66(10), 2157-2170.
Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2022). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 425-487.
Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2022). Algorithm selection on a meta level. Machine learning.
Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence
2021
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings: PAKDD 2021: Advances in Knowledge Discovery and Data Mining (Vol. 12712, pp. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12712 LNAI).
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In ECML/PKDD workshop on Automating Data Science (ADS 2021)
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2021). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3055-3066. [9347828].
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.
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3037-3054. [9321731].
2020
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. In U. Schmid, D. Wolter, & F. Klügl (Eds.), KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Proceedings (Vol. 12325, pp. 59-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12325 LNAI).
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. In A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Eds.), Discovery Science - 23rd International Conference, DS 2020, Proceedings: DS 2020: Discovery Science (Vol. 12323, pp. 309-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12323 LNAI).
Tornede, A., Wever, M., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In Proceedings of The 12th Asian Conference on Machine Learning
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. (4th Workshop on Meta-Learning at NeurIPS 2020).
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2020). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In Lecture Notes in Computer Science: IDA 2020: Advances in Intelligent Data Analysis XVIII
2019
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft
Tornede, A., Wever, M., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In 29th Workshop Computational Intelligence
ris.uni-paderborn.de/download/15011/17060/ci_workshop_tornede.pdf
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
Wever, M., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. In ICML 2019 Workshop AutoML
ris.uni-paderborn.de/download/10232/13177/Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
Projects
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ERC Starting Grant: Interactive and Explainable Human-Centered AutoMLTrust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: (i) Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems. (ii) Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained. These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for: (iii) Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact. (iv) Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0. Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.Led by: Prof. Dr. Marius LindauerTeam:Year: 2022Funding: EUDuration: 2022-2027
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