Participating working groups - Computer Science
This page lists all working groups of the Department for Mathematics and Computer Science / Division Computer Science whose research focuses fit the ideas of the key profile area "Intelligent Methods for Earth System Sciences".
The research group “Theory of Algorithms” focusses on the design and analysis of algorithms for combinatorial optimization problems. A particular focus is on uncertainty in the data. We also analyze the loss in solution quality caused by the uncertainty.
...Read more
The primary research objective is the development and application of modern deep learning methods to Earth System Data, i.e. weather, air quality and climate data. Furthermore, the team exploit the rapid technical and conceptual development of web services and large-scale computing infrastructure to open new ways for analyzing complex Big Earth system data from observations and models. This research always keeps in mind the possible operational application, for example in the DestinE context.
...Read more
The research of the group "Efficient Algorithms" is in the field of Algorithms, with a focus on Sublinear-time algorithms, and Approximation algorithms. They are interested in the design and analysis of efficient algorithms that work on large high-dimensional datasets, detect and exploit long range structure in the data, and are robust to perturbations, noise and erasures in the input.
Areas of Research:
- High Performance Computing
- Distributed Computing
- Operating Systems
- Modelling and Simulation
- Big Data
...Read more
Our leading research objective: Help engineers understand how software-intensive systems will behave in reality
We are actively doing reserach in the areas Requirements Engineering, Software Engineering & Machine Learning, Explainable Intelligent Systems, Research Software Engineering, Data-driven Systems Engineering and Model-based Systems Engineering. Almost each area is leaded by one of our team members.
The research group Trustworthy Artificial Intelligence Lab (TAIL) is led by Prof. Dr. Aleksander Bojchevski. Broadly speaking this research is about models and algorithms that are not only accurate or efficient, but also robust, uncertainty-aware, privacy-preserving, fair, and interpretable. In short the goal is to make models trustworthy. To increase trust we study how to provide guarantees, e.g. robustness certificates and conformal prediction sets. One focus area of our research is trustworthy graph-based models such as graph neural networks. We like graphs because graph data is everywhere: neural connections in the brain, social networks, interactions between proteins, molecules, code, the structure of web and much more.
...Read more
The group activities comprise research and teaching in the area of Interactive Visualization and Visual Analytics.
We combine interactive visualization with data analysis techniques for decision making. We develop new visual-interactive techniques for
- Data exploration
- Data analysis
- Data presentation and publishing