William Thistleton Email & Phone Number | Dr. William Thistleton is an Associate Professor of Mathematics at SUNY Polytechnic Institute where he teaches a wide range of graduate and undergraduate classes in analysis, probability, statistics, design of experiments, and data science. After serving in the United States Peace Corps as a math and physics teacher in Cameroon, he completed his graduate studies at Stony Brook University in the area of Computational Fluid Dynamics and Ground Water Modeling. He has served as Department Chair and as Program Coordinator of the Applied Math major, as well as coordinator of the Graduate Advanced Certificate in Data Analysis. He develops and delivers numerous workshops for area teachers, employees, and students. His current research interests include machine learning and deep learning, in particular generative processes. He has applied image processing to help understand vascular geometry and its effect on retinal hemodynamics; the use of nonextensive entropy and statistics in simulation, including the Generalized Box-Muller Method for Generating q-Gaussian Random Deviates; architecture and its impacts on the health of long term care residents; as well as early work in web centered learning. Dr. Thistleton has worked as a consultant at a number of companies, where he has developed price models and provided summaries and analysis of market research; developed, implemented, and analyzed studies to determine the effects of site relocation on patient care and staff attitudes in long term care; worked to understand psychosocial domains in Fibromyalgia; studied a multidisciplinary program to benefit returning veterans; developed and implemented algorithms in multiple computational environments including C, R, Java, MATLAB, and Python for parameter estimation, bitstream analysis, numerical methods, and analysis of correlated systems. He developed a modified Box Müller algorithm for pseudorandom q-Gaussian deviates. He has developed and implemented optimization and ensemble learning algorithms on classical computing and quantum annealing (D-Wave) platforms. Participated in the National Science Foundation NSF I-Corp program at Cornell University. An early and aggressive proponent of distance education and has developed traditional online courses as well as a successful Coursera course in Time Series Analysis. Designs and delivers workshops in Data Science for professional development at the corporate level. Delivered a distance workshop in Machine Learning to the New York State Master Teacher Program. He is a SUNY Online Teaching Ambassador.

William Thistleton

Dr. William Thistleton is an Associate Professor of Mathematics at SUNY Polytechnic Institute where...
Utica, New York, United States

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William Thistleton’s Email thistlet@sunypoly.edu

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William Thistleton’s Location Utica, New York, United States

William Thistleton’s Current Industry Suny Polytechnic Institute

William Thistleton’s Prior Industry Us Peace Corps

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Work Experience

Suny Polytechnic Institute

Associate Professor Of Mathematics

1993-09-01 00:00:00 - Present

Us Peace Corps

Math and Physics Teacher, Government High School Limbe

1983-06-01 00:00:00 - 1985-07-01 00:00:00

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FAQ About William Thistleton

William Thistleton is located at
Utica, New York, United States

William Thistleton is a Dr. William Thistleton is an Associate Professor of Mathematics at SUNY Polytechnic Institute where he teaches a wide range of graduate and undergraduate classes in analysis, probability, statistics, design of experiments, and data science. After serving in the United States Peace Corps as a math and physics teacher in Cameroon, he completed his graduate studies at Stony Brook University in the area of Computational Fluid Dynamics and Ground Water Modeling. He has served as Department Chair and as Program Coordinator of the Applied Math major, as well as coordinator of the Graduate Advanced Certificate in Data Analysis. He develops and delivers numerous workshops for area teachers, employees, and students. His current research interests include machine learning and deep learning, in particular generative processes. He has applied image processing to help understand vascular geometry and its effect on retinal hemodynamics; the use of nonextensive entropy and statistics in simulation, including the Generalized Box-Muller Method for Generating q-Gaussian Random Deviates; architecture and its impacts on the health of long term care residents; as well as early work in web centered learning. Dr. Thistleton has worked as a consultant at a number of companies, where he has developed price models and provided summaries and analysis of market research; developed, implemented, and analyzed studies to determine the effects of site relocation on patient care and staff attitudes in long term care; worked to understand psychosocial domains in Fibromyalgia; studied a multidisciplinary program to benefit returning veterans; developed and implemented algorithms in multiple computational environments including C, R, Java, MATLAB, and Python for parameter estimation, bitstream analysis, numerical methods, and analysis of correlated systems. He developed a modified Box Müller algorithm for pseudorandom q-Gaussian deviates. He has developed and implemented optimization and ensemble learning algorithms on classical computing and quantum annealing (D-Wave) platforms. Participated in the National Science Foundation NSF I-Corp program at Cornell University. An early and aggressive proponent of distance education and has developed traditional online courses as well as a successful Coursera course in Time Series Analysis. Designs and delivers workshops in Data Science for professional development at the corporate level. Delivered a distance workshop in Machine Learning to the New York State Master Teacher Program. He is a SUNY Online Teaching Ambassador.