Big Data Algorithms

The group conducts fundamental research on the design and analysis of prior-free algorithms, with a strong emphasis on theoretical foundations and mathematical methodology. The research agenda is shaped by current developments such as the slowing of Moore’s Law and the increasing importance of “big” and “fast” data. Particular attention is given (a) to distributed and dynamic algorithms that operate efficiently in complex and evolving environments and (b) to integrating machine learning approaches with traditional algorithm design techniques. Graphs form a central methodological framework, serving as an abstract model for a wide range of real-world networks, including communication systems, social structures, and infrastructure networks.

Computational Geometry

The Computational Geometry group develops efficient geometric algorithms and data structures that connect mathematical foundations with practical computational challenges. Its research addresses fundamental geometric structures and algorithmic techniques for processing and analyzing complex spatial data. A particular emphasis is placed on exact and robust computation, ensuring reliability and numerical precision even in challenging geometric settings. The group’s work spans both foundational and application-oriented perspectives, translating theoretical insights into scalable and dependable solutions.

Computational Systems

The Computational Systems Group focuses on the engineering of systems software and on advancing approaches to computer science education that make complex concepts more accessible. The group combines research on fundamental aspects of computer systems with innovative teaching methods that emphasize transparency and conceptual clarity. Teaching spans the full stack of computing systems at bachelor’s and master’s level, from machine architecture and low-level system concepts through programming languages, compilers, and virtual machines to higher-level perspectives such as cloud computing, integrating core topics including algorithms, data structures, complexity, and computability. A central component of the educational approach is Selfie, an open educational system featuring a self-compiling C compiler, a self-executing RISC-V emulator, and a self-hosting RISC-V hypervisor, supported by source code, teaching materials, and automated assessment.

Computer Science Didactics

The Computer Science Didactics group works at the intersection of computer science, education, and digital transformation. It is responsible for teacher education in computer science and digital education, supporting the preparation of future educators and contributing to the development of digital competencies in schools. The group’s research aims to advance STEM education through innovative approaches to teaching and learning. A central focus lies in exploring how digital literacy and computational thinking can be meaningfully integrated into educational practice, combining theoretical perspectives with practical implementation. By connecting research, teacher education, and curriculum development, the group seeks to create engaging learning environments and to support sustainable innovation in computer science education.

Database Systems

The Database Group conducts research on all aspects of data management. Its work is particularly driven by applications that are heavily data-based but cannot be fully supported by current systems due to the complexity and richness of their queries, or privacy concerns. The group’s research focuses on queries over complex objects and massive data collections, as well as on data cleaning and integration, indexing techniques, query processing and optimization, distributed data management in disaggregated memory architectures, and numerical computations in databases. Another focal point is providing high utility private query responses on complex-structured data sets such as streams based on variations of the differential privacy framework. Many research questions are motivated by challenges arising in concrete application domains, such as process mining, digital humanities, or cognitive neuroscience. The outcomes of this research include new algorithms with performance guarantees that are implemented and evaluated in the context of their motivating applications.

Efficient Algorithms

The Efficient Algorithms group studies algorithmic aspects of modern models of computation, with a focus on parallel and distributed computing. These paradigms are central to addressing large-scale and computationally intensive problems in science and engineering, where tasks must be coordinated across multiple processors or distributed systems. Research focuses on fundamental challenges in large networks such as information dissemination, network exploration, efficient load balancing, and the structural and spectral properties of graphs modeling real-world networks. The main goal is to design and analyze efficient scalable algorithms for the problems mentioned before.

MATRIS

The “Mathematics for Testing, Resilience and Information Security” (MATRIS) Research Group focuses on the mathematical foundations of complex, resilient and secure systems. Its research centers on combinatorial designs and their applications to software testing, in particular combinatorial testing, as well as on methods for improving robustness and reliability in complex systems. Further work addresses challenges in disaster management, symbolic computation, optimization algorithms, and all mathematical aspects of information security. By combining discrete mathematics with algorithmic techniques, the group develops rigorous methods that support dependable software, secure infrastructures, and resilient systems.

MATRIS in its dual structure operating as a joint research lab between University of Salzburg and FH Salzburg, conducts cutting-edge research on the following research areas:

Software Engineering

The Software Engineering group investigates principles and methods for developing reliable and maintainable software systems, guided by the conviction that many existing systems are unnecessarily complex and difficult to manage. The research therefore focuses on concepts, methodologies, and tools that support the construction of lean and robust software. This includes work on software design and implementation, reuse and composition, and programming methodologies that promote clarity, efficiency, and long-term sustainability. A pragmatic research approach connects these foundations with application-driven domains, currently with an emphasis on Artificial Intelligence (AI-)based application such as a bot platform that allows the fine-grained tuning of AI characters and avatars, as well as research on the implications of vibe coding for software development and teaching computer science.