Index

Synthetic Non-Contrast CT Angiography Image Generation using Deep Learning Methods

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.

Latent Space Modeling for Event Detection in Power Grid Data

This project explores how latent representations learned from raw grid waveforms can reveal underlying structure and enable early detection of abnormal events. By modeling high-frequency voltage and current signals, we aim to distinguish critical disturbances from normal behavior with minimal delay.

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