Paul Pearson


Education

Ph.D in Mathematics June 2006

Northwestern University, Evanston, Illinois

Thesis: The mod p homology of topological modular forms

Advisor: Paul Goerss


BA in Mathematics May 2001

Saint Olaf College, Northfield, Minnesota

Honors: Phi Beta Kappa, summa cum laude, departmental distinction


Work Experience

Associate Professor July 2018 - present

Department of Mathematics, Hope College, Holland, Michigan


Assistant Professor July 2014 - June 2018

Department of Mathematics, Hope College, Holland, Michigan


Visiting Assistant Professor July 2012 - June 2014

Department of Mathematics, Hope College, Holland, Michigan


Visiting Assistant Professor August 2009 - May 2012

Department of Mathematics, Fort Lewis College, Durango, Colorado


Visiting Assistant Professor January 2007 - July 2009

Department of Mathematics, University of Rochester, Rochester, New York


Visiting Research Scholar September 2006 - November 2006

Mittag-Leffler Institute, Stockholm, Sweden


Instructor September 2005 - June 2006

Department of Mathematics, Northwestern University, Evanston, Illinois


Teaching Assistant September 2001 - June 2005

Department of Mathematics, Northwestern University, Evanston, Illinois


Publications

McMorris, D., Pearson, P., & Yurk, B. (2016). A modified wavelet method for identifying transient features in time signals with applications to bean beetle maturation. Involve, a Journal of Mathematics, 10(1), 21-42.

Pearson, P. T. (2016). The mod 2 Hopf ring for connective Morava K-theory. Journal of Homotopy and Related Structures, 11(3), 469-491.

Pearson, P. T. (2014). The connective real K-theory of Brown-Gitler spectra. Algebraic & Geometric Topology, 14(1), 597-625.

Pearson, P. T. (2013). Visualizing clusters in artificial neural networks using Morse theory. Advances in Artificial Neural Systems, 2013 (Article ID 486363), 1-8.

Pearson, P. T., & Cooper, C. I. (2012, September). Using self organizing maps to analyze demographics and swing state voting in the 2008 US presidential election. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 201-212). Springer, Berlin, Heidelberg.

Cooper, C. I., & Pearson, P. T. (2012). A genetically optimized predictive system for success in general chemistry using a diagnostic algebra test. Journal of Science Education and Technology, 21(1), 197-205.