Vladimir Cherkassky
Professor

  

Area of expertise: Machine learning, data mining, statistical learning theory

Education
Ph.D., 1985, University of Texas, Austin, TX, United States
M.S., 1976, Moscow Aviation Institute, Moscow, Russia

Contact information
Office: 6-111 Keller Hall
Telephone: (612) 625-9597
E-mail: cherkass (at) umn.edu
Personal Web Site: http://www.ece.umn.edu/~cherkass/

Honors/Awards
2008 The A. Richard Newton Breakthrough Research Award from Microsoft Research
2007 Fellow of IEEE
1997, 1998 IBM Partnership Award

Synopsis
My research interests include pattern recognition, statistical learning theory, and artificial neural networks. This is also known as predictive learning, where the goal is to estimate a good predictive model from available data. Predictive learning broadly overlaps with data mining, statistical estimation, signal processing, and artificial intelligence. I am interested in both theoretical foundations of statistical learning, and various practical applications.

Selected publications
Cherkassky, V. and Y. Ma. "Multiple Model Regression Estimation". IEEE Transactions on Neural Networks, 16.4 (2005): 785-798.

Cherkassky, V. and S. Kilts. "Myopotential denoising of ECG signals using wavelet thresholding methods". Neural Networks, 14 (2001): 1129-1137.

Cherkassky, V. and F. Muller. "Learning from Data: Concepts, Theory, and Methods". Wiley, (1998).