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://www.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).