Graphical systems integrate knowledge and methods from psychology and informatics, with particular emphasis on perception and computer graphics. Low-level psychophysics will be combined with modern computer graphics to allow an ecological psychology approach to modelling and synthesizing behaviourally relevant, spatiotemporal information. Perceptual knowledge and methods will be linked to computer science techniques to create effective and efficient computer vision algorithms.

Major focuses are:

• Facial emotion perception and animation
• Dynamic event classification or dynamic object recognition
• Computational aesthetics: Calculating and using image statistics for art generation, classification, and restoration

Database and Information Systems comprises in particular the storage, administration, processing and evaluation of large data sets. In detail, the basics of managing, storing and searching data in database and information systems are covered, e.g. the integration of heterogeneous data stocks in data warehouses and the extraction of new knowledge from these inventories using data mining algorithms.

Content-based similarity search using the laws of quantum logic in unstructured databases is another focus of the field. The aim is to find exactly those objects in the database that have the highest relevance with regard to an information need specified by the user. Main topics are the use are the use of effective comparison functions that reflect the human sense of similarity , the use of suitable techniques to increase the efficiency of complex similarity calculations and the processing of large data sets. This is extended by possibilities to improve the query results through user interactions (relevance feedback).

Major focuses in the framework of quantum-logic-inspired machine learning are:

• Classification (Deep neural networks, Decision trees, Ensembles learning)
• Reinforced learning
• XAI: Explainable Artificial Intelligence

Computer Engineering focuses its research on the design, architecture, and implementation of energy-efficient computing systems. Main research topics are:

• Development of new methods and techniques for the conception and realization of heterogeneous computer architectures, especially using reconfigurable logic devices and microcontrollers
• Design of novel processors using non-volatile memory technologies (RRAMs)
• Development of modern methods and frameworks for the design of energy-efficient embedded computing systems as well as their programming

Our research approaches are used in diverse applications such as modern industrial processes (including predictive maintenance), fast data transmission (5G), health applications (wearable sensors for diagnostics) and for fast signal processing (optical, radar) using artificial intelligence.

Programming Languages and Compiler Construction represent classic computer science research fields. The aim is to provide languages that allow a human friendly and correct modelling of problem solutions, as well as an efficient translation into machine language.

The main research areas of the programming languages and compiler group include languages and programming paradigms and methods for compilation and interpretation as well as constraint-based programming. Constraint programming is a part of artificial intelligence and aims at modelling and solving very complex search and optimization problems. In this area we investigate and develop modelling techniques and new problem-solving methods.

The research area of IT Security focuses on network security and online privacy. The main vision is to advance the state of the art in research and to educate qualified computer scientists in the area of IT Security who are able to meet the challenges of the growing demand on securing IT Systems and provide data protection in various areas of our life and society.

Major focuses are:

• Anonymous communication
• Privacy enhancing technologies
• Artificial intelligence and machine learning for attacks' detection
• Wireless security, IoT security
• Secure communication in untrustworthy environments
• Traffic analysis, Fingerprinting
• Self-learning anomaly detection in network traffic and structures
• Passive monitoring approaches for restrictive networks

The main vision of our research is to design and implement practical modelling and analysis techniques for developing and maintaining high-quality software systems.  By tightly integrating flexibly transformable and incrementally verifiable models into the development process, we can manage the increasing complexity and heterogeneity of today's data- and software-intensive systems and the increased demands on their adaptability with a clear commitment to quality.

Since the underlying structure of models can be described by graphs, graph transformation is a suitable means to specify model transformations. Moreover, graph transformation lends itself to the management and evolution of semi-structured data. We therefore use graph and graph transformation theory as solid enablers for the interplay of modelling, transformation and verification techniques.

Based on these foundations, we continuously strive to develop and implement quality assurance techniques incorporating fundamental engineering principles such as expressiveness, efficiency, usability, and generality. The overall goal is to create quality assurance techniques tightly intertwined with the software development process and comprising an appropriate degree of automation and human interaction.

Current and future example challenges focus primarily on advanced automation of design and quality assurance of data- and software-intensive systems. We intend to focus on medical informatics as an important representative in this field.

The conceptual design and development of resilient distributed systems is highly complex and very difficult. The research area of Wireless Systems deals with the modelling of interdependent system attributes, such as reliability and security or reliability and power consumption. These system properties are “non-functional” and due to this often neglected until too late.  The focus of our research is to take these features and their interdependencies with others into account when modelling single systems as well as networked devices. The goal is to gain a deeper and more profound understanding of these dependencies in order to provide functional models and develop suitable design tools for such systems.

The research group Applied Mathematics is mainly dealing with mathematical image processing and its applications. From a methodological point of view, the main focus is on mathematical methods in artificial intelligence as well on models derived from partial differential equations and their numerics as well as optimization methods. Furthermore, the mathematical modelling of new processes and the construction of efficient numerical methods for applications in computer science, natural sciences or engineering sciences are of particular interest.

Modelling of real-world processes often involves high-dimensional stochastic differential equations with multiple time and length scales. In many cases, the largest scales give the relevant behaviour in the system, the direct numerical simulation of which is difficult. Therefore, major focuses are model reduction and optimal control of high-dimensional stochastic differential equations with multiple scales and their application in science and engineering.

In particular, research deals with the interface between computational probability and dynamical systems. It includes the multiscale analysis of complex systems, stochastic control theory, and the stochastic simulation of high-dimensional differential equations that appear in science and engineering applications.

The research focus of Numerical Mathematics and Scientific Computing is the numerical modelling and simulation of fluid dynamic problems in nature and technology by using computational fluid dynamics. This includes the following:

• Computational fluid dynamics (CFD) simulations of multiphase systems and turbulent combustion
• Low-dimensional stochastic modelling of combustion processes and multiphase flows
• Model development for reactive and non-reactive fluid dynamic systems
• High-performance computing

The focus of research is on solving mathematical problems from banking, insurance and the energy industry. Optimal decisions have to be made under uncertainty, risk and return have to be coordinated and new products have to be evaluated. Tools from probability theory, mathematical statistics, stochastic analysis and stochastic optimal control are used extensively for this.

Major focuses are:

• Stochastic financial mathematics
• Portfolio optimisation
• Optimal control of energy storages
• Stochastic epidemic models and control
• Actuarial mathematics
• Differential equations with random parameters and applications, e.g. random oscillations, heat conduction in random media