Vladimir Cherkassky


Research Area: Biomedical and Biological Computational Methods, Devices, and System; Communications, Signal Processing, and Networking; Computer Engineering, VLSI, and Circuits

6-111 Keller Hall

Area of Expertise:

Statistical learning, data mining and neural network systems


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


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


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.


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).