Theses Supervision | Prof. Dr. Pascal Kerschke | Department of Information Systems

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Prof. Dr. Pascal Kerschke
Former Research Assistant

Data Science: Statistics and Optimization

Leonardo Campus 3
48149 Münster

kerschke.github.io/

External profiles:
ResearchGate

ShortURL: ERC.IS ShortURL

erc.is/p/kerschke

Supervised Theses
- Simeon Brüggenjürgen: Mixture of Decision Trees for Interpretable Machine Learning (Master's thesis IS, 2022)
- Jonathan Heins: Feature Learning for the TSP - Investigating the Capabilities of Deep Learning for Automated Algorithm Selection (Master's thesis IS, 2022)
- Christopher Patrick Olbrich: Developing and Investigating Problem-Specific Sequential Approaches for Automated Algorithm Selection (Master's thesis IS, 2021)
- Lennart Schäpermeier: Multimodal Search Structures in Continuous Multi-Objective Optimization (Master's thesis IS, 2021)
- Kevin Patrick Sy Lim: Development of a Hybrid CMA-ES by Means of Exploratory Landscape Analysis and Machine Learning (Master's thesis IS, 2021)
- Investigating the Applicability and Limitations of Deep Learning for Book-Guided Speech Synthesis (Master's thesis IS, 2021)
- An Empirical Study on the Benefits of Multiobjectivisation for Solving Single-Objective Problems (Bachelor's thesis IS, 2020)
- Arne Von Berg: Investigating Common Optimization Strategies of Deep Learning (Master's thesis IS, 2020)
- Tobias Mai: Analyzing Alternative Approaches for the State-of-the-Art Dimensionality Reduction Technique t-SNE (Master's thesis IS, 2019)
- Automated Data-Agnostic Global Explanations of Machine Learning Models by Means of AutoAnchors (Master's thesis IS, 2019)
- Christian Reil: A Comparative Study of Modern Feature Selection Strategies and Alternative Approaches (Master's thesis IS, 2019)
- Anh-Quoc Martin Hoang: Model-Driven Algorithm Selection - An Interactive Analysis Tool for Assessing Algorithm Selection Scenarios (Master's thesis IS, 2019)
- Oliver Hampel: Optimizing Algorithm Configuration by Improved Initial Heterogeneous Parameter Sampling (Master's thesis IS, 2019)
- Raphael Patrick Prager: Algorithm Configuration of CMA-ES Based on Exploratory Landscape Analysis and Machine Learning (Master's thesis IS, 2019)
- Martin Walter: Investigating Methods for Model-Agnostic Explainability of Machine Learning Algorithms (Master's thesis IS, 2019)
- Moritz Vinzent Seiler: Using Interpretability of Deep Learning Neural Networks for Investigating the Effects of Adversarial Permutation (Master's thesis IS, 2018)
- Christian Hanster: Development of an interactive GUI for the R-Package flacco (Bachelor's thesis IS, 2017)

Supervised Theses