Computer Science

In terms of its existing expertise, Computer Science at the University of Groningen is well positioned to make a significant contribution to the Centre. There are several groups working in Image Processing & Computer Vision that are developing state-of-the-art morphological image operators for feature extraction and description from very large images and image sequences. This expertise can be harnessed to develop efficient image analysis algorithms to analyse conductivity levels and conduction paths in images of nanomaterials.

Computer Science has a long tradition in developing biologically motivated and brain-inspired pattern recognition and machine-learning methods, which is directly relevant to Cognitive Computing. Expertise in Computer Graphics, Visualization and Visual Analytics can be applied to the visualization of computational infrastructure (system, pipelines and networks), processes and data in order to support the interactive design of cognitive materials and gain insight into the complex processes and structures involved. Systems engineering expertise is available for the design of very complex, scalable and/or distributed systems-of-systems, such as cognitive systems that comprise heterogeneous and operationally independent constituent systems.

In Fundamental Computing, the expertise covers areas such as logic, discrete structures, advanced algorithms and data structures, and the formal modelling of communicating systems. This knowledge is needed to develop new computing paradigms, algorithms and programs for the new cognitive systems, and to understand the computational complexity of such systems in a precise mathematical sense.

There are also cross-links with complementary expertise in AI in relation to machine learning, automated reasoning and human-computer interaction. Collaboration with the materials scientists has already been established for the design of cognitive materials (efficient image analysis algorithms for analysing conductivity levels and conduction paths during network training). Other direct contributions will involve the use of machine learning and pattern recognition in cognitive system design, interactive network and system visualization and parameter inference in complex systems.