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Peter Bajorski

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

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

Peter Bajorski, Ph.D., is a faculty member in the Graduate Statistics Department in the Center for Quality and Applied Statistics at the Rochester Institute of Technology. His duties include teaching graduate courses in statistics, designing and teaching short courses, and performing consulting and research for wide range of corporate clients. Prof. Bajorski holds a Ph.D. degree in Mathematical Statistics and a B.S. and M.S. from the Technical University of Wroclaw, Poland, and an M.Eng. in Industrial Engineering from the University of Buffalo. Prior to joining RIT, he held positions at Cornell University, the University of British Columbia, and Simon Fraser University. He was also an Associate Statistician at the Engineering Research and Development Bureau, New York State Department of Transportation.

Dr. Bajorski’s statistical expertise is in regression techniques, multivariate analysis, design of experiments, and nonparametric methods. He has done statistical work in Imaging Science, Reliability, Environmental Conservation, Transportation, Health Services, Quality Assurance, as well as in Material Engineering, Civil Engineering, and Industrial Engineering. He teaches Six Sigma Black Belts and Green Belts and is familiar with the Lean Enterprise approach to process improvement.

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 has received several awards for his contributions to research and teaching. He has served as a referee for 12 professional journals and wrote numerous book reviews for book publishers and for professional journals. 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.

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