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Peter Bajorski

Peter Bajorski
Graduate Chair-Center for Quality & Applied Statistics, Professor

Phone: 585-475-7889
Office: HLC/2544

Professor Peter Bajorski received his B.S./M.S. in mathematics from the University of Wroclaw, Poland, and his Ph.D.  in mathematical statistics from the Technical University of Wroclaw, Poland. He has taught undergraduate and graduate courses in various areas of mathematics and statistics, including his course on multivariate statistics for imaging science.

Dr. Bajorski’s research interests are in creating new statistical methods for solving applied problems in various fields, and in investigating properties of known statistical approaches in a new applied context, so that the methods can be used in a correct way. Jointly with Prof. Peter Hall, he developed a new methodology and theory for nonnegative-score principal component analysis. In the area of subpixel target detection in hyperspectral images, Dr. Bajorski introduced a methodology of generalized detection fusion and created a theory for performance evaluation of detectors. In the area of stochastic modeling of networks, he developed a new methodology for modeling multi-step connections using Markov chains.

Dr. Bajorski has done mathematical and statistical work in many fields, including imaging science, reliability, environmental conservation, transportation, health services, quality assurance, as well as in material engineering, civil engineering, and industrial engineering.

Dr. Bajorski has published over 60 research papers in scientific journals with international circulation and has given more than 70 talks to the professional community. He is a senior member of IEEE and SPIE. He is also past-president of the Rochester Chapter of the American Statistical Association.

Dr. Bajorski’s book on Statistics for Imaging, Optics, and Photonics was published in the prestigious Wiley Series in Probability and Statistics in 2011.

Selected Publications

  1. P. Bajorski, P. Hall, H. Rubinstein, “Methodology and Theory for Nonnegative-Score Principal Component Analysis,” Statistica Sinica , Vol 23, No. 2, pp. 963-988, 2013.
  2. P. Bajorski, “Generalized Detection Fusion for Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, Volume 50, Issue 4, pp: 1199 - 1205, 2012.
  3. P. Bajorski, “Statistical Inference in PCA for Hyperspectral Images,” IEEE Journal of Selected Topics in Signal Processing, Vol. 5, Issue 3, pp. 438-445, 2011.
  4. P. Bajorski, “Second Moment Linear Dimensionality as an Alternative to Virtual Dimensionality,” IEEE Transactions on Geoscience and Remote Sensing, Volume 49, Issue 2,  pp: 672-678,  2011.
  5. P. Bajorski, “Analytical comparison of the matched filter and orthogonal subspace projection detectors for hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing,  Volume 45,  Issue 7,  Part 2,  Page(s):2394 – 2402, July 2007.
  6. P. Bajorski and J. Petkau, “On the Efficiency of Non‑Parametric Tests for Comparing Two Groups Based on Changes in an Ordered Categorical Response Variable,” Journal of the American Statistical Association, Vol. 94, No. 447, pp. 970-978, Sept. 1999.
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