Universiteit Leiden

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Computer Systems, Imagery and media (CSI)

The Computer Systems, Imagery & media (CSI) program performs research on methods and techniques for the design, implementation and application of advanced computer systems, in particular parallel, distributed and embedded computer systems. Focus is furthermore on methods and techniques for the analysis and synthesis of images, pattern recognition, image fusion, 3D reconstruction and visualization, computer vision, imaging systems, image search and media technology.

This research programme of the Leiden Institute of Advanced Computer Science (LIACS) contains three themes.

Computer systems

Researchers at LIACS are working to develop the computer systems of tomorrow. These include high performance computers, capable of simultaneously processing huge volumes of data. In that respect, we are involved in research into grand database systems and in embedded systems, the driver of the internet of things.

High performance computing

A high performance computer or supercomputer owes its massive processing capacity to the fact that it chops a single overarching task into a whole series of smaller tasks. It simultaneously tackles each of those smaller problems. This parallel computing approach makes the system much faster and more efficient. We also work on distributed computing, in which systems perform independent tasks, meanwhile communicating about it among themselves.

Database systems

Data analytics and big data all for other demands on data technology than do the more conventional transactions. We are looking for ways to save the ever growing databases and, at the same time, keep them manageable. Our database system, MonetDB, uses a column store system instead of row store. MonetDB belongs to the worldwide top in data analytics databases.

Embedded systems

From mobile phones through to space probes: an embedded system in which sensors and processors are integrated, makes them ‘smart’. Change the circumstances and they will change their behaviour. They are able to communicate with us and with each other. Our embedded systems research is closely related to the development of the internet of things. Furthermore, we work on field programmable gate array, reconfigurable chips that are specifically suitable for embedded systems.

Media & creativity

This is where artificial intelligence and machine learning meet philosophy, cognitive science, and the creative arts. Examples of research questions in this domain are: 'Can an algorithm be creative by human standards?', 'Can creative processes, such as composing music or writing poetry, be modelled?', and 'How can algorithms optimally support human creativity?'


Applications and research directions include interaction models, augmented/virtual reality, computer-generated art, computational research of language and literature, gaming, and 'public technology' like the systems in smartphones, 3D-printers, robots, and drones. We also include (serious) games: virtual and augmented worlds make such tasks as skill-straining more enjoyable for both children and adults.

Research goals and output

Within our Media & Creativity thema we emphase on personal curiosity and on conducting research in creative and playful ways. Research conducted by both staff and students regularly leads to practical spin-offs in society and entrepreneurship, or to exhibitions in museums and public space.

Computer vision

On the basis of the characteristic aspects of a picture, certain computers can tell us what the picture is showing. They can learn this in the same way that young children are able to learn to recognise images. Further improving these techniques opens the way to a whole range of new applications. This is a field in which we are carrying out much research work.

Neural networks

For years already, we have been trying to train computers in image recognition. By now, much more is possible, due to the development of convolutional neural networks. Through this kind of networks, enormous amounts of labelled images give constant feedback during an image recognition task. This makes it possible for the computer to learn faster.

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