statistical signal processing, pattern recognition, machine learning.


signal processing and machine learning.


Miroslaw Pawlak received the Ph.D. and D.Sc. degrees in computer engineering from Wroclaw University of Technology, Wroclaw, Poland. He is currently a Professor at the Department of Electrical and Computer Engineering, University of Manitoba.
Dr. Pawlak is a registered Professional Engineer with the Engineers Geoscientists Manitoba.
He has held a number of visiting positions in North American, Australian, and European Universities. He was at the University of Ulm and University of Goettingen as an Alexander von Humboldt Foundation Fellow. His research interests include statistical signal processing, machine learning, and nonparametric modeling. Among his publications in these areas are the books Image Analysis by Moments (Wroclaw Univ. Press, 2008), and Nonparametric System Identification (Cambridge Univ. Press, 2010), coauthored with Prof. Greblicki. Dr. Pawlak has been an Associate Editor of the Journal of Pattern Recognition and Applications, Pattern Recognition, International Journal on Sampling Theory in Signal and Image Processing, Opuscula Mathematica and Statistics in Transition-New Series. Dr. Pawlak name has appeared on the World’s Top 2% Scientists list published recently by Stanford University.

Graduate Student Opportunities

Dr. Pawlak does not have any graduate opportunities at this time.

Selected Publications

M. Pawlak and J. Lv, 2022, “Nonparametric testing for Hammerstein systems“, IEEE Trans. on Automatic Control, vol. 67, pp. 4568-4584.

M. Pawlak and U. Stadtmuller, 2020, “Nonparametric specification testing for signal models“, IEEE Trans. Information Theory, vol. 66, pp.6434– 6448.

W. Greblicki and M. Pawlak, 2019, “The weighted nearest neighbor estimate for Hammerstein system identification‘, IEEE Trans. on Automatic Control, vol. 64, pp.1550–1565.

W. Greblicki and M. Pawlak, 2017, “Hammerstein system identification with the nearest neighbor algorithm“, IEEE Trans. on Information Theory, vol.63, pp.4746–4757.

D. Rzepka, M. Pawlak, D. Koscielnik and M. Miskowicz, 2017, “Bandwidth estimation from multiple level-crossings of stochastic signals“, IEEE Trans. on Signal Processing, vol. 65, pp. 2488–2502.

W. Mo, J. Lv, M. Pawlak, U. D. Annakkage, H. Chen, 2020, “Power system oscillation mode prediction based on the LASSO method“, IEEE Access, vol. 8, pp. 101068 – 101078.

J. Lv, M. Pawlak and U. Annakkage, 2017 ,“Prediction of the transient stability boundary based on nonparametric additive modeling“, IEEE Trans. Power Systems, vol. 32, pp. 4362–4369.

M. Mehrjoo, M.Jafari Jozani and M. Pawlak, B. Bagen, 2021, “A multi-level modeling approach towards wind farm aggregated power curve“, IEEE Transactions on Sustainable Energy, vol. 12, pp. 2230-2237.

M. Pawlak and U. Stadtmuller, 2018, “On certain operators associated with Hermite and Laguerre polynomials“, Applicationes Mathematicae, vol. 45, pp.71–90.

M. Pawlak, G.S. Panesar, M. Korytkowski, 2021, “A novel method for invariant image reconstruction “, Journal of Artificial Intelligence and Soft Computing Research, vol. 11, pp. 69–80.

W. Greblicki and M. Pawlak. Nonparametric System Identification, Cambridge University Press, Cambridge, 2010, ISBN-13: 9780521868044.