If you are interested in working with us, send us email including information about relevant courses and projects that you have conducted, and your interests.
External ressources
- Course on scientific writing
- Thesis template, courtesy of Prof. Holger Dell
- Guidelines for bachelor thesis (B.S. bioinformatics)
- Guidelines for master thesis (M.S. bioinformatics)
Completed Master Theses
- Hoang Tu Ngo (2020), Understanding human visual perception by finding similarities between fMRI brain data and deep neural networks.
- Elias Poensgen (2021), Cross-Modal Learning and Generation using Generative Adversarial Nets.
- Lukas Licitar (2021), How does a new automated AI-technology change the role in client engagement, by SAP Concur and is it possible to derive a direct increase in revenue? Possible conclusion from KPIs and analysis. (At SAP Concur)
- Teresa Werthmann (2021), Ethical assessment of AI systems in healthcare: A use case.
- Frederike Laufenberg (2021), Ethical assessment of AI systems in healthcare: A use case.
- Daniel Pietschmann (2021), Single-task and multi-task transfer learning in a multi-source context.
- Yannic Vorpahl (2021), Single-task and multi-task transfer learning in a multi-source context.
- David Lüdke (2022). Neural Flow-based Deformations for Statistical Shape Modelling. (At Zuse Institute Berlin)
Completed Bachelor Theses
- Felix Nonnengießer (2020), Glancing at Images: Foveated Neural NetworksWith Recurrent Visual Attention for Image Recognition.
- Marius Bange (2020), Vimotion - A Webapp for emotion based matching of audio and video.
- Quang Anh Hong (2021), Influence of Training Dataset Resemblance to Stimulus Set on Prediction Accuracy of Brain Activity.
- Nizar Bikti (2021), Visual Features: Their Extraction and Linear Correlation to Emotional Dimensions.
- Carla Frenzel (2021), Predicting Human Brain Activity During Language Processing with the Help of Computational Language Processing Models.
- Sandesh Baral (2021), Keypoints regression of dance poses with Openpose and Neural networks based dance classification methods.
- Shivan Oskan (2021), A Web App to Analyse the Impact of Dance Videos on Emotion Perception by Viewing and Mood Regulation by Imitation.
- Yusuf Baran (2021), Which characteristics of a dance are responsible for the emotion the spectator feels?
- Domenic Bersch (2021), Towards a general library for deep learning models to understand the architecture of the visual cortex.
- Bipul Mani Pokhrel (2021), Feature Comparison of BERT Transfer Learning with autoencoder language models.
- Tomislav Vrdoljak (2021), A Machine Learning Approach Using Deep Learning to Evaluate Epileptic Treatment Based on EEG Data. (With Prof. Jochen Triesch)
- Max Krawietz (2021), Do Machine Learning classifiers have an innate fairness?
- Dirk Neuhäuser (2021), Contrastive Learning of Object Representations.
- Ali Poursotoudeh Tehrani (2021), Do Convolutional Neural Networks Encode Numerosity Information?
- Jonas Roemer (2022), Application of Genetic Algorithms to Generative Adversarial Networks.
- Snizhana Fedchenko (2022). Management of Machine Learning Models for Estimation of Flight Risk of Employees. (At Merk KGaA)
- Tra Mi Thi Tran (2022), The influence of dataset biases on classification fairness.
- Matthias Fulde (2022). Predicting Neural Responses to Naturalistic Videos using Multimodal deep neural networks.
- Johannes Bastian (2023). Development of Number Sense in Neural Networks in a Real World Environment.
Design a free website with Mobirise