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Fields of Interest

We consider and analyze machine learning problems from the perspective of complexity theory. We try to determine the information or computational complexity of learning problems, and to design efficient learning algorithms. Furthermore, we investigate combinatorial optimization problems and explore the possibility of designing efficient approximation algorithms. We have a general interest in finding new programming methods and in inventing new data structures. We are furthermore interested in any results that shed more light on the famous P-NP problem.

Computational geometry. We study the design and analysis of efficient algorithms and data structures for geometric problems. In particular, we are interested in shape matching, that is developing algorithms for determing the similarity of geometric shapes. Furthermore, we are interested in applications of computational geometry to geographic data, in particular to movement data.