Neural Processes for Optimal Sensor Placement
Machine learning models for optimal ocean measurement locations using uncertainty-aware Neural Processes.
University of Rostock · Institute for Visual and Analytic Computing
Marine Data Science
Research group working at the intersection of machine learning, marine data, sensor systems, probabilistic modeling, and visual computing.
Member of the CORE Network (Cognitive Robotics in Europe)
Research
Machine learning methods for marine data, sensor systems, medical imaging, tabular data, and human activity recognition.
Ultrasound Image Analysis
AI-driven real-time 3D ultrasound analysis for diagnostic and therapy guidance.
Deep Learning for Tabular and Categorical Data
Deep neural network models for tabular and categorical data beyond tree-based baselines.
Domain Adaptation for Human Activity Recognition
Domain adaptation methods for human activity recognition across different sensor datasets.
Projects
Funded research projects.
Data4Sim
Sensor-based capture and simulation of manual logistics processes for testing operational changes before implementation.
AI4Pumps
AI-based speed control for pump systems that predicts efficient operating points from sensor and pulsation data.
Show2Instruct
Multimodal generative AI for turning natural-language interaction with complex software systems into machine-processable instructions.
For students
Theses
We are always looking for motivated students to work on Bachelor and Master thesis topics.
- medical imaging
Open Applications in Medical Image Computing
Students interested in pursuing a thesis related to medical applications are welcome to contact Daniel Wulff.
- marine
Deep learning-based classification of phytoplankton in imaging flow cytometry data
- machine learning
Methods for Pseudo-Label Selection for Student-Teacher-based Domain Adaptation
Methods for selecting pseudo-labels in semi-supervised learning scenarios to improve model performance with unlabeled data.
- medical imaging
Uncertainty-Aware Stenosis Segmentation in Digital Subtraction Angiography using Probabilistic Deep Learning
Probabilistic deep learning for segmentation uncertainty in angiography.
Teaching
Current lectures and student projects.
EgoProject 2026
Summer semester group project on spatial understanding in egocentric data.
Artificial Intelligence (UBB Cluj-Napoca)
Artificial Intelligence teaching material for UBB Cluj-Napoca.
RoOT
RoOT Summer School material in Rostock.
People
Current team members.
Feyza Eksen
PhD student
Corinna Burmeister
Team Assistant
Peter Eschholz
Systems Engineer