
Vladimir Cherkassky
Professor
Research Area: Biomedical and Biological Computational Methods, Devices, and System; Communications, Signal Processing, and Networking; Computer Engineering, VLSI, and Circuits
6-111 Keller Hall | |
612-625-9597 | |
cherk001@umn.edu | |
http://people.ece.umn.edu/~cherkass/ |
Area of Expertise:
Statistical learning, data mining and neural network systems
Education:
Ph.D., 1985, University of Texas, Austin, TX, United States
M.S., 1976, Moscow Aviation Institute, Moscow, Russia
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.
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).