Humboldt-Universität zu Berlin - Faculty of Mathematics and Natural Sciences - Department of Computer Science

Focus Areas

The Department of Computer Science focuses on three key research areas, each of which comprises several research groups.

Data and Knowledge Engineering

The focus area "Data and Knowledge Engineering" unites the research activities of several chairs at the institute that work on different fields regarding the foundations, construction, and applications of algorithms and tools for managing large, volatile, and possibly unstructured data sets. The participating groups together cover this field in a comprehensive manner, starting from the logical foundations of query languages over indexing structures, algorithms for the analysis of network and streaming data, and natural language processing to applied data science in commercial and scientific applications.

Algorithms and Structures

Modeling and problem specifications in computer science are usually accomplished by a combination of logical specifications and discrete mathematical structures. The research subjects in this focus are the fundamental principles underlying the efficient solvability of problems in various application areas of computer science. We are interested in a wide range of complexity measures, including various measures for computational complexity (How difficult is it to solve the problem algorithmically?) as well as for descriptive complexity (How difficult is it to describe the problem in a suitable formalism?). Moreover, we apply the principles of algorithm engineering to build bridges in different application areas. This focus area builds on the participants' proven expertise in the areas of (experimental and theoretical) algorithmics, logic, and complexity theory.

Model-based system development

Complex hardware and software systems are increasingly being developed and analyzed on the basis of models. With a model and its technical or organizational environment, the properties of the system can be examined prior to implementation, for example its conformity with the intended, intuitively formulated behavior and its correct interaction with other systems. Model-based system development is a long-term effective technology; it increases quality, reduces costs, and affects almost all aspects of the design process. Using realistic models, the performance of systems can also be optimized during operation, and the reaction to unforeseen events can be examined. The teaching and research groups involved in this focus area develop suitable methods and tools for the modeling, development, and analysis of large IT systems.