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Peter Bajorski

Peter Bajorski

Phone: 585-475-7889
Office: HLC/2532

Dr. Peter Bajorski has taught undergraduate and graduate courses in fundamentals of probability and statistics, multivariate analysis, regression analysis, and experimental design. He has also developed and taught a specialized course on multivariate statistics for imaging science.

Education: Ph.D. in Mathematical Statistics, Technical University of Wroclaw, Poland

Research Interests: Imaging Science, Network Communication, Biomedical Applications, High-Dimensional Data

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 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.
  1. P. Bajorski, “Generalized Detection Fusion for Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, Volume 50, Issue 4, pp: 1199 - 1205, 2012.
  1. 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.
  1. 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.
  1. 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.
  1. 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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