Research

Our research is aimed at developing novel methods, techniques, and tools for the engineering of dependable and efficient large-scale systems, including cloud-based systems, cyber-physical systems, and systems for data science and scientific computing, spanning the following research areas:

1. Software Architecture, focussing on the design, modeling, and simulation of large-scale distributed system architectures with autonomic and self-adaptive resource management,
2. Systems Benchmarking, focussing on experimental analysis of performance, scalability, energy efficiency, and dependability properties,
3. Predictive Data Analytics, focussing on the engineering of data science workflows and systems for machine learning and artificial intelligence (AI), including time series forecasting, anomaly detection, and critical event prediction,
4. Cyber Security, focussing the design, testing, and performance evaluation of adaptive security architectures and homomorphic computing techniques for privacy-preserving computations.

Orthogonal to these research areas, our past and ongoing research projects span the following application and technology domains:

1. Cloud computing, virtualization, containerization, software-defined data centers,
2. IoT and cyber-physical systems in the domains of transportation/logistics, Industry 4.0, and robotics,
3. Scientific computing and high-performance data analytics for earth observation and sustainability research,
4. Medical computer science.

Our research is inspired by the vision of Self-Aware Computing Systems, which are systems designed with built-in model learning and reasoning capabilities enabling autonomic and proactive decision making at run time. The following pages provide an overview of our long-term vision and research agenda as well as industry cooperations: Vision , Industry Cooperations, Awards and Recognitions, Publications.