Rate My Professor Vladimir Cherkassky

Vladimir Cherkassky is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota Twin Cities and an adjunct or affiliate faculty member in the Department of Computer Science and Engineering within the College of Science and Engineering. His academic interests center on statistical learning, data mining, neural network systems, and predictive learning from data. He has authored over 120 technical papers and book chapters covering topics such as computer networks, modeling and optimization, statistical learning, and artificial neural networks. Cherkassky serves on the graduate faculty for Data Science programs and has been principal investigator on projects including image compression for digitized images and statistical analysis of soil chemical survey data.

Cherkassky co-authored the seminal book Learning from Data: Concepts, Theory, and Methods (Wiley, 1998) with F. Mulier, and edited From Statistics to Neural Networks: Theory and Pattern Recognition Applications (Springer-Verlag, 1994). Key publications include "Multiple Model Regression Estimation" in IEEE Transactions on Neural Networks (2005, with Y. Ma); "Myopotential denoising of ECG signals using wavelet thresholding methods" in Neural Networks (2001, with S. Kilts); "Model complexity control and statistical learning theory" in Natural Computing (2002); "Model selection for regression using VC generalization bounds" in IEEE Transactions on Neural Networks (1999, with X. Shao, F. Mulier, and V. Vapnik); "Measuring the VC-dimension using optimized experimental design" in Neural Computation (2000, with X. Shao and W. Li); and "Signal estimation and denoising using VC-theory" in Neural Networks (2001, with X. Shao). He received the IBM Faculty Partnership Award in 1996 and 1997. A Senior Member of the IEEE and member of the International Neural Network Society (INNS), he served on the INNS Governing Board from 1996 to 1998. Cherkassky acted as Associate Editor for IEEE Transactions on Neural Networks in 1998 and Guest Editor for its special issue on VC Learning Theory and Its Applications in 1999. He is on the editorial boards of Neural Networks, Natural Computing: An International Journal, and Neural Processing Letters. In 1993, he organized and directed the NATO Advanced Study Institute From Statistics to Neural Networks: Theory and Pattern Recognition Applications in France. He has delivered numerous tutorials and invited talks on statistical and neural network methods for learning from data at conferences across Europe, North America, and Asia.