Guest staff

prof.dr. H. Trautmann (Heike)

Onbezoldigd hoogleraar
EEMCS-CS-DMB
h.trautmann@utwente.nl
+31534896112
Visitekaartje

I am Guest Professor of Data Science: Statistics and Optimization at the University of Twente and Professor of Machine Learning and Optimization at the Department of Computer Science, Paderborn University, Germany. I am member of the European Research Center for Information Systems (ERCIS) and head of the ERCIS competence center Social Media Analytics as well as the ERCIS research cluster 'Data Science and AI'.

My research mainly focuses on Data Science, Data Stream Mining, Social Media Analytics, Algorithmization and Social Interaction, Automated Algorithm Selection and Configuration as well as (Multiobjective) Evolutionary Optimization. 

Expertises

  • Computer Science

    • Optimization Problem
    • Algorithms
    • Single Objective
    • Continuous Optimization
    • Multiobjective
    • Social Media
    • Algorithm Selection
    • Detection

Organisaties

Nevenwerkzaakheden

  • Paderborn University , GermanyProfessor of Machine Learning and Optimisation

Please have a look at my complete publication list :-)

Publicaties

2024
On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation ProblemsIn Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (pp. 305-321). Preuß, O. L., Rook, J. & Trautmann, H.https://doi.org/10.1007/978-3-031-56852-7_20Exploratory Landscape Analysis for Mixed-Variable Problems. ArXiv.org. Prager, R. P. & Trautmann, H.https://doi.org/10.48550/arXiv.2402.16467Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Prager, R. P. & Trautmann, H.https://doi.org/10.1162/evco_a_00341Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems. ArXiv.org. Seiler, M. V., Kerschke, P. & Trautmann, H.https://doi.org/10.48550/arXiv.2401.01192
2023
Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape FeaturesIn FOGA 2023: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 129-139). Association for Computing Machinery. Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H. & Mersmann, O.https://doi.org/10.1145/3594805.3607136Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer ProblemsIn GECCO 2023 Companion: Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 451-454). Association for Computing Machinery. Prager, R. P. & Trautmann, H.https://doi.org/10.1145/3583133.3590757The objective that freed me: a multi-objective local search approach for continuous single-objective optimization, 271–285. Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H. & Grimme, C.https://doi.org/10.1007/s11047-022-09919-wA study on the effects of normalized TSP features for automated algorithm selection, 123-145. Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H. & Kerschke, P.https://doi.org/10.1016/j.tcs.2022.10.019Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSPIn 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 361-368). IEEE. Seiler, M. V., Rook, J., Heins, J., Preuß, O. L., Bossek, J. & Trautmann, H.https://doi.org/10.1109/SSCI52147.2023.10372008Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis FeaturesIn Applications of Evolutionary Computation : 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (pp. 411-425). Springer. Prager, R. P. & Trautmann, H.https://doi.org/10.1007/978-3-031-30229-9_27Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto SetsIn Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings (pp. 291-304). Springer. Schäpermeier, L., Kerschke, P., Grimme, C. & Trautmann, H.https://doi.org/10.1007/978-3-031-27250-9_21
2022
Process-Oriented Stream Classification Pipeline: A Literature Review, Article 9094. Clever, L., Pohl, J. S., Bossek, J., Kerschke, P. & Trautmann, H.https://doi.org/10.3390/app12189094Hpo X Ela: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. ArXiv.org. Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H. & Kerschke, P.https://doi.org/10.48550/arXiv.2208.00220

Onderzoeksprofielen

I am study coordinator of the executive master program "Data Science" (University of Münster Professional School). This is a joint program of UT and the University of Münster.

In my previous role of Vice Dean of Internationalization at the University of Münster, I supported two Double Degree Agreements between UT and UM:

Verbonden aan opleidingen

Vakken collegejaar 2023/2024

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.

Vakken collegejaar 2022/2023

Lopende projecten

Reducing the moderation effort of user comments with the help of automation using text analytical methods (MODERAT!)

In recent years, a rapid increase in racist, political and religiously motivated hate commentary has led many newspaper editors to deactivate their online comment functions on their websites. While this is understandable from an economic point of view for the individual publishers, serious problems for the public discourse arise in view of restriction quotas of up to 50%. The MODERAT! project aims to use an integrative and interdisciplinary approach to develop software tools and a web platform that will enable operators to moderate web debates with significantly less effort. Comments are analyzed automatically, so that only a small number of critical comments have to be viewed manually. In this way, media houses and publishers should be able to offer web debates again on their own websites and thus enter into a more active exchange with the readership.

COSEAL - Configuration and Selection of Algorithms

The COSEAL research group is an international consortium of researchers from all over the world which addresses current challenges from Algorithm Selection, Algorithm Configuration and Machine Learning.

Benchmarking Network

The Benchmarking Network is an initiative that has emerged in summer 2019, with the idea to consolidate and to stimulate activities on benchmarking iterative optimization heuristics such as local search algorithms, swarm intelligence techniques, model- and/or surrogate-based heuristics, etc - in short, all algorithms that work by a sequential evaluation of solution candidates.

Algorithmization and Social Interaction

Topical Program at the University of Münster, Germany

Imagine you call a company and your request is no longer answered by a human being but by an artificial assistant – how does this affect you as an individual and society at large? And does the customization of information in social media and online environments limit our horizon, or even keeps us in a “filter bubble”? These are just a few of the socially and politically relevant core questions of the interdisciplinary topical program “Algorithmization and Social Interaction”.

DemoRESILdigital: Democratic Resilience in Times of Online-Propaganda, Fake News, Fear- and Hate speech

This junior research group is supported by the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia. The group is associated with the Department of Communication and the Department of Information Systems of the University of Münster, Germany.

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