University of Minnesota

200 Union Street S.E.

Minneapolis, MN 55455

tel (612) 625-9597 email cherkass @ece.umn.edu

Prof. Cherkassky published over 120 technical papers and book chapters in the areas of computer networks, modeling and optimization, statistical learning and artificial neural networks. His current research is on theory and applications of methods for predictive learning from data, and he has co-authored a monograph Learning From Data published by Wiley in 1998.

Prof. Cherkassky is a senior member of IEEE and a member of the International Neural Network Society (INNS). He served on the Governing Board of INNS from 1996 to 1998. He served as Associate Editor of IEEE Transactions on Neural Networks (TNN) in 1998. He is currently on editorial board of Neural Networks (the official journal of INNS), Natural Computing: An International Journal and Neural Processing Letters. He was a Guest Editor of the IEEE TNN Special Issue on VC Learning Theory and Its Applications published in September 1999.

Dr. Cherkassky was organizer and Director of NATO Advanced Study Institute (ASI) From Statistics to Neural Networks: Theory and Pattern Recognition Applications held in France in 1993. He presented numerous tutorials and invited talks on statistical and neural network methods for learning from data at various conferences/scientific meetings in Europe, North America and Asia. He received IBM Faculty Partnership Award in 1996 and 1997 for his work on learning methods for data mining.

1. V. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory and Methods, Wiley Interscience, 1998 .
2. V. Cherkassky , J.H. Friedman and H. Wechsler (Eds.), From Statistics To Neural Networks. Theory and Pattern Recognition Applications, NATO ASI Series F, v.136, Springer-Verlag, 1994 .
3. V. Cherkassky, X. Shao, F. Mulier and V. Vapnik, Model selection for regression using VC generalization bounds, IEEE Trans on Neural Networks, 10,5, 1999, 1075-1089
4. X. Shao, V. Cherkassky and W. Li, Measuring the VC-dimension using optimized experimental design, Neural Computation, MIT Press, 2000, 12, 8, 1969-1986
5. Cherkassky and X. Shao, Signal estimation and denoising using VC-theory, Neural Networks, Pergamon, 14, 2001, 37-52
6. Cherkassky, Model complexity control and statistical learning theory, Natural Computing, Kluwer,1,2002, 109-133.