The “Algorithmics” working group mainly researches efficient algorithms for combinatorial optimisation problems. Algorithm design focusses on the correctness and theoretical runtime analysis of the algorithms. If no efficient algorithm can be found, approximation algorithms and fixed-parameter algorithms can be used for special input data. Algorithm engineering is about practical implementation, optimisation and experimental evaluation, often for problems from interdisciplinary projects. The group also offers courses in other theoretical areas.
The group’s research focuses on the development and application of modern bioinformatics methods and programme packages, mostly in close collaboration with academic and industrial partners. The areas of application of our research include computer-aided diagnosis and prognosis as well as rational drug design. The group actively contributes to several large software projects, such as the Biochemical Algorithms Library (BALL) or the OpenMS framework. In teaching, we offer regular bioinformatics courses as well as the undergraduate courses “Introduction to Programming” (EiP) and “Introduction to Software Development” (EiS).
New developments in the field of computer graphics are immediately recognisable to everyone from current computer-animated films or computer games. Prof. Dr Elmar Schömer’s Computational Geometry working group researches the theoretical foundations for efficient geometric algorithms and data structures and uses these in practical applications for the visualisation and simulation of motion sequences. This also includes applications from the fields of computer-aided design, automated motion planning and packaging optimisation.
Data management is currently more important than ever and presents us with major challenges; today’s data is available in a variety of formats, it is merged into enormous volumes of data and comes at an extremely high speed from a variety of sources. The explosion of large volumes of data has made it necessary to research and develop systems that support extremely low latency and real-time data analytics. In addition, today’s data is becoming increasingly complex as it can be routinely enriched with different and diverse types of non-traditional data such as geospatial, text, time and graph data. Due to their heterogeneity and different semantics, such complex data types require special handling, including effective storage, indexing and processing. The Data Management working group deals with the challenges that arise in the in-memory management of complex big data.
The working group mainly conducts research in the fields of data mining and machine learning. Methods are developed to analyse large and complex amounts of data and to apply these methods in the life sciences and other fields (computational sustainability, energy, transport, social networks, …). The courses include lectures, seminars and practicals in the core areas of data mining and machine learning, but also lectures from other areas of applied computer science such as database systems and software engineering.
The structured and generally educational teaching of concepts, models and ideas of computer science is the core task of computer science didactics. In particular, this takes place as part of the various teacher training programmes for grammar schools and vocational schools. Another important concern of computer science education at JGU is to make the ideas of computer science accessible to the general public, for example through hands-on exhibitions and general lectures.
André Brinkmann’s research group focuses on research in the fields of storage systems and scalable computer systems. High performance computing (HPC) will move into the area of exascale computing in the coming years and will present exciting challenges. HPC is in the process of transitioning from a purely computing power-centred area to an area in which data management is playing an increasingly important role. This means that not only computing power, but also storage capacity and storage bandwidth must be designed to be scalable.
This group focuses on the design, implementation and evaluation of scalable tools for genome sequence analysis (bioinformatics) and computational science applications using modern high performance computing (HPC) technologies, e.g. manycore architectures (e.g. CUDA-enabled GPUs), multicore architectures, heterogeneous clusters and FPGAs. The methods and tools are often developed in collaboration with interdisciplinary partners at Johannes Gutenberg University, including the Department of Biology, the Department of Medicine and the Institute of Molecular Biology.
The High Performance Computing and its Applications (HPC|A) group focuses on scientific research and development in the areas of parallel file and storage systems, modular supercomputing, performance engineering, high performance computing and networking, reproducible benchmarking, parallel I/O, and parallel programming models.
Moreover, we are driving the ongoing development and establishment of JGU’s HPC division as a national high-performance computing center, coordinating HPC activities at both regional and national levels. We also represent JGU in key HPC-focused committees, including the NHR Alliance, Gauss Alliance, ETP4HPC, and EOFS.
The Information Systems working group is concerned with the design and implementation of innovative methods and systems for the efficient management, processing, analysis and interpretation of data. This includes the adaptation of established database concepts to modern (hardware/software) environments, the design of unconventional processing models (such as blockchains) and the transactional and analytical processing of very large and potentially differently structured data volumes (OLTP/OLAP). The working group not only looks at individual components and processes in these subject areas in isolation, but always focuses on the combination of components to create an efficient overall system.
Language processing is concerned with the development of computer models that can understand and generate text in natural languages (such as German, English or Japanese). Some well-known examples are machine translation systems or dialogue systems. Prof Dr Katharina von der Wense’s NALA working group deals with a variety of subtopics of language processing. Some of the group’s current areas of interest are languages with limited data sets, computational morphology, fairness, privacy, machine translation and language processing for education or medical applications.
Programming languages are probably the most important tool for developing software. The Programming Languages working group aims to support developers in the creation and maintenance of reliable and efficient software by researching new programming languages, programming tools and programming methods. In particular, we research approaches to software development in which linguistic abstractions and domain-specific languages (DSLs) are used. We deal both with the question of how such abstractions and languages can best be designed and with the question of how a language design can best be realised. We supplement this with research into new methods for static program analysis, with the help of which errors in the code and potential for program optimisation can already be found during compilation. We are concerned with both the construction of verifiably correct analyses and their incremental execution.
The working group deals with the optimisation of athletic performance with the help of computer models. Sports Informatics in Mainz deals with both group tactical optimisation in team sports (e.g. football) and individual performance optimisation in individual sports (e.g. running and cycling).
The working group conducts research and teaching in the overlap of computer graphics (synthesis of images of virtual scenes) and computer vision (pattern recognition in data from the real world). Our core expertise lies in recognising patterns and structures in geometric and spatial data. The group offers various courses related to the research area, as well as in the broader field of computer graphics and vision, and in undergraduate education in computer science.
