Pattern Recognition & Bioinformatics

Research area

Pattern recognition is concerned with processing raw measurement data by a computer to arrive at a prediction, which can then be used to formulate a decision or action to take. Problems to which pattern recognition are applied have in common that they are too complex to model explicitly, thus requiring algorithms to learn parameters in generic models from limited sets of examples. Pattern recognition practice is firmly focused on real-world, sensor-based applications. This places it at the core of the current process of scientific discovery, by allowing researchers to derive regularities in large amounts of data in areas as diverse as physics, biology and geology, but also psychology and neuroscience. Pattern recognition algorithms also find application in industrial and consumer settings, allowing machines to sense the environment and to decide on actions or support human decision making. The PRB section studies both aspects in three different research labs. One research lab (the pattern recognition laboratory) focuses on the foundations of pattern recognition: representation and generalization, in which new ways of describing objects and learning from examples are studied. Three other research labs (the vision lab, the bioinformatics lab and the socially perceptive computing lab) apply these techniques in the domains of images, molecular biology and behaviour.