Theses Supervision | Prof. Dr. Fabian Gieseke | Department of Information Systems

VCard

QR-Code

Prof. Dr. Fabian Gieseke
Professor

Data Science: Machine Learning and Data Engineering

Leonardo Campus 3
48149 Münster

Room: 233

Phone: +49 251 83-38151
fabian.gieseke@wi.uni-muenster.de

ShortURL: ERC.IS ShortURL

erc.is/p/fabian.gieseke

Supervised Theses
- Semi-Supervised Machine Learning for Query By Example on Relational Databases (Master's thesis IS, 2022)
- Collecting personal learner information in digital education: From tedious questionnaire to entertaining chatbot experience (Master's thesis IS, 2022)
- Web tracking and its application for Learning Analytics: A case study with reveal.js (Master's thesis IS, 2022)
- Jan Pauls: Generating High-Resolution Height Maps Using Deep Neural Networks (Master's thesis IS, 2022)
- Linus Stach: Application of Transferable Adversarial Attacks on Convolutional Neuronal Networks: An Evaluation of Existing Attack and Defense Mechanisms (Bachelor's thesis IS, 2022)
- Detection and Segmentation of Tree Instances on a Rwandan Satellite Dataset (Bachelor's thesis IS, 2022)
- Customer Behavior Predictions for Online Travel Agencies: A Practical Implementation (Master's thesis IS, 2022)
- Data provenance for a semantically-structured representation of license information for open educational resources (Bachelor's thesis IS, 2022)
- Erik Zimmermann: Development of a gradient-free adversarial attack on convolutional neural networks to evaluate existing defense strategies. (Bachelor's thesis IS, 2022)
- Deep Learning Semantic Segmentation of Tree Stock in South Africa Using Satellite Images (Bachelor's thesis IS, 2022)
- Experimental Improvement of Streaming Quality in a Cloud-Based Geographical Information System (Bachelor's thesis IS, 2022)
- Machine Learning Under Resource Limitations (Bachelor's thesis IS, 2021)
- Efficient Convolutional Neural Network Inference on Microcontrollers (Bachelor's thesis IS, 2021)
- Reverse image search using deep discrete feature extraction and locality-sensitive hashing (Bachelor's thesis IS, 2021)
- Thomas Ackermann: Investigating Deep Learning-based Strategies to Cope with Label Noise in Satellite Data Using the Example of African Non-Forest Trees (Master's thesis IS, 2021)
- Semantic Segmentation of Satellite Images Using Point Supervision (Master's thesis IS, 2021)
- Investigating the Practicability of Deep Learning based Super- and Upsampling Approaches for Satellite Data (Bachelor's thesis IS, 2021)

Supervised Theses