Theoretische Informatik / Formale Methoden

Universität Kassel » Fachbereiche » FB 16 - Elektrotechnik / Informatik » Informatik

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Wilhelmshöher Allee 73
34121 Kassel

Beschreibung

The research group was established at the School of Electrical Engineering and Computer Science of the University of Kassel in April 2010 and originally named "Formal Methods and Software Verification". As of November 2013 it became the research group "Theoretical Computer Science / Formal Methods". The main research agendas comprise areas like computational logic with a particular emphasis on modal and temporal logics, model checking and game theory for program verification and analysis, abstraction, etc.

The research group contributes to the computer science Bachelor and Master curricula by regularly offering courses and seminars in the areas of logic for computer science, program verification, etc., as well as Bachelor and Master projects related to current research topics in such areas.

Publikationen

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2024	Huseljic, D., Herde, M., Rauch, L., Hahn, P., Huang, Z., Kottke, D., Vogt, S., Sick, B., 2024. Efficient Bayesian Updates for Deep Learning via Laplace Approximations. NeurIPS. https://doi.org/10.48550/arXiv.2210.06112
2023	Huang, Z., He, Y., Herde, M., Huseljic, D., Sick, B., 2023. Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets. CEUR Workshop Proceedings 3470, 25–45.
2023	Herde, M., Huseljic, D., Sick, B., Bretschneider, U., Oeste-Reiß, S., 2023. Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning, in: Bunse, M., Hammer, B., Krempl, G., Lemaire, V., Tharwat, A., Saadallah, A. (Hrsg.), International Workshop & Tutorial on Interactive Adaptive Learning (IAL). CEUR Workshop Proceedings (RWTH Aachen), Torino, Italy, S. 14–18.
2022	Kumari, V., Harirchian, E., Lahmer, T., Rasulzade, S., 2022. Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings. Buildings 12, 1–23. https://doi.org/10.3390/buildings12050578
2022	Herde, M., Huang, Z., Huseljic, D., Kottke, D., Vogt, S., Sick, B., 2022. Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. arXiv Preprint. https://doi.org/10.48550/arXiv.2210.06112
2021	Hanika, T., Hirth, J., 2021. Exploring Scale-Measures of Data Sets, in: Braud, A., Buzmakov, A., Hanika, T., Le Ber, F. (Hrsg.), Formal Concept Analysis. Springer International Publishing, Berlin/Heidelberg, S. 261–269. https://doi.org/10.1007/978-3-030-77867-5_17
2021	Hanika, T., Hirth, J., 2021. Quantifying the Conceptual Error in Dimensionality Reduction, in: Braun, T., Gehrke, M., Hanika, T., Hernandez, N. (Hrsg.), Graph-Based Representation and Reasoning. Springer International Publishing, Cham, S. 105–118. https://doi.org/10.1007/978-3-030-86982-3_8
2021	Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B., 2021. Toward optimal probabilistic active learning using a Bayesian approach. Machine Learning 110, 1199–1231. https://doi.org/10.1007/s10994-021-05986-9
2020	Herde, M., Kottke, D., Huseljic, D., Sick, B., 2020. Multi-annotator Probabilistic Active Learning, in: ICPR (Hrsg.), International Conference on Pattern Recognition (ICPR). TBD, S. TBD. https://doi.org/10.1109/ICPR48806.2021.9412298
2020	Kastaun, M., Meier, M., Hundeshagen, N., Lange, M., 2020. ProfiLL – Professionalisierung durch intelligente Lehr-Lernsysteme , in: Kaspar, K., Becker-Mrotzek, M., Hofhues, S., König, J., Schmeinck, D. (Hrsg.), BILDUNG, SCHULE, DIGITALISIERUNG. Waxmann, Münster, S. TBD.

Weitere Forschungsaktivitäten

09/2021	Professionalisierung angehender Biologie-Lehrkräfte zum Diagnostizieren von Schülerhür-den beim Experimentieren über den Einsatz intelligenter Lehr-Lernsysteme Posterbeitrag
06/2021	Professionalisierung durch intelligente Lehr-/Lernsysteme (ProfiLL) – Ein Kooperationspro-jekt zwischen Theoretischer Informatik und Fachdidaktik Biologie zur Gestaltung innovati-ver, digitalgestützter (Hochschul-)Lehre. Posterbeitrag