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.