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The group investigates structural aspects of fundamental problems in Computer Science and their mathematical models. This leads to the design of better algorithms protocols and systems as well as understanding of their implicit computational limits. Specific areas of interests include: algorithm design, data structures, string algorithms, computational complexity, combinatorial optimization, coding and information theory, machine learning. Most results obtained are in the intersection of algorithmics with several other areas in theory and applications, including bioinformatics, communication networks, operating research and artificial intelligence.
Algorithmic Bioinformatics and Natural Computing
Application of theoretical methods and data analysis to model information underlying biological processes: graph and string algorithms for systems biology; advanced data structures for sequence data; distance measures for biological sequences; natural (biotechnological, membrane) computing; pattern recognition, machine learning for biomedical data.