Index

RPA-Bots zur Prozessautomatisierung im Workflow Management der DATEV eG

Advanced Machine Learning Models for Leakage Detection and Localization in Water Distribution Networks Using Real-System Data

Reinforcement Learning for Centralized Fault Coordination in Power Systems

In this project, we develop a hybrid reinforcement learning framework for adaptive protection in power grids with high DER penetration. A centralized model is first trained using system-wide current, voltage, and impedance data to coordinate both primary and backup relays, followed by decentralized fine-tuning using only local measurements to ensure autonomous operation in case of communication loss. The approach aims to improve relay coordination, robustness, and decision-making by exploring different recurrent network architectures such as RNN and LSTM.

Transformer-Based Forecasting Model for Fault Detection in Power System Protection

This project explores transformer-based forecasting models for intelligent fault detection in electrical power systems. The approach reframes fault detection as a prediction problem, where the model learns the normal temporal dynamics of high-frequency voltage and current waveforms and identifies deviations as anomalies. By doing so, it avoids the heavy reliance on labeled data that limits many existing machine learning approaches in protection systems.

The study evaluates several transformer architectures on a large set of physics-based simulations that represent realistic grid conditions, fault types, and operating scenarios. The results demonstrate that prediction-based attention models can achieve high detection accuracy and robustness, even under scarce data and varying grid configurations. This work provides a promising foundation for more adaptive, data-efficient, and resilient protection schemes in future power networks.

Report Generation in pathology using WSIs

This project focuses on developing methods for processing large-scale digital pathology datasets and extracting meaningful features from whole slide images to support automated report generation. Emphasis is placed on efficient handling of gigapixel image data and preparing it for use in vision-language models for clinical applications.

Evaluating Large Language Models Using Gameplay (ClemBench)

Exploring Species-level Similarity in Bayesian Stimulus Priors of Artificial Intelligent Agents

Deep Learning-based Classification of Body Regions in Intraoperative X-Ray Images

Automated Patient Positioning (MRI) using nnUNet

Diffusion Transformer for CT artifacts compensation

Computed Tomography (CT) is one of the most important modality in modern medical imaging, providing invaluable cross-sectional anatomical information crucial for diagnosis, treatment planning, and disease monitoring. Despite its widespread utility, the quality of CT images can be significantly degraded by various artifacts arising from physical limitations, patient-related factors, or system imperfections. These artifacts, manifesting as streaks, blurs, or distortions, can obscure critical diagnostic details, potentially leading to misinterpretations and compromising patient care. While traditional iterative reconstruction and early deep learning methods have offered partial solutions, they often struggle with complex artifact patterns or may introduce new inconsistencies. Recently, diffusion models have emerged as a powerful generative paradigm, demonstrating remarkable success in image synthesis and restoration tasks by progressively denoising an image from a pure noise distribution. Concurrently, Transformer architectures, with their inherent ability to capture long-range dependencies via self-attention mechanisms, have shown promise in various vision tasks. This thesis investigates the potential of  Diffusion Transformer, for comprehensive CT artifact compensation. By synergizing the iterative refinement capabilities of diffusion models with the global contextual understanding of Transformers, this work aims to develop a robust framework capable of effectively mitigating a wide range of CT artifacts, thereby enhancing image quality and improving diagnostic reliability. This research explores the design, implementation, and rigorous evaluation of such a model, comparing its performance against existing state-of-the-art techniques.