Christian Borgelt
Position:
Principal Researcher of the Research Unit "Intelligent Data Analysis and Graphical Models"
E-mail:
La dirección de correo electrónico está protegida contra los robots de spam, necesita
tener Javascript activado para poder verla.
Biography:
Christian Borgelt received his diploma (M.Sc.) in computer science from the University of Braunschweig, Germany, in 1995. After spending a year at the Daimler-Benz Research Center Ulm, he became a Ph.D. student at the University of Magdeburg, Germany, and received his Ph.D. in computer science in 2000. He was awarded the Ph.D. Prize 2000 and the Research Prize 2002 of the faculty of computer science of the University of Magdeburg, Germany. In 2006 he received the venia legendi for computer science, again from the University of Magdeburg, Germany.
He published about 90 papers in national and international conferences, workshops, books, and journals, co-authored two books (one on graphical models and one on neuro-fuzzy systems) and co-edited two conference proceedings (IDA 2003 and GfKl 2005) as well as a special journal issue (Int. Journal of Approximate Reasoning). He is also well known in the data mining community for a large number of free implementations of data mining algorithms, including frequent item set mining, decision tree induction, artificial neural network training, Bayes classifier induction, regression, graphical model learning etc.
His current research interests are very diverse and include molecular and graph mining, (approximate) frequent item set mining, graphical models for diagnosis and planning, learning graphical models from data, prototype-based classification and clustering and other intelligent data analysis methods.
He published about 90 papers in national and international conferences, workshops, books, and journals, co-authored two books (one on graphical models and one on neuro-fuzzy systems) and co-edited two conference proceedings (IDA 2003 and GfKl 2005) as well as a special journal issue (Int. Journal of Approximate Reasoning). He is also well known in the data mining community for a large number of free implementations of data mining algorithms, including frequent item set mining, decision tree induction, artificial neural network training, Bayes classifier induction, regression, graphical model learning etc.
His current research interests are very diverse and include molecular and graph mining, (approximate) frequent item set mining, graphical models for diagnosis and planning, learning graphical models from data, prototype-based classification and clustering and other intelligent data analysis methods.
