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Erich McAlister is the chair and a professor for the Department of Mathematics at Fort Lewis College. He joined the college in 2005. Dr. McAlister’s research background is in the area of C*-star algebras. He has presented his research on mathematics and teaching at colloquiums and meetings around the country. He regularly directs students’ independent study projects and senior seminar research, the culminating academic experience for mathematics majors at Fort Lewis. Such collaborations have produced research on topics such as digraphs, finite topological spaces, group representations, numerical ranges, frames, and differential games.
Dr. McAlister has received funding from a Title III Mathematics Foundation STEM success grant to redesign mathematics courses to improve student success and retention, and better service students from applied disciplines.
“Multiresolution Equivalence and Path-Connectedness,” Numerical Functional Analysis and Optimization, coauthor with Dr. Veronika Furst, 2014
“Crossed Products of Noncommutative CW complexes by Finite Groups,” Rocky Mountain Journal of Mathematics, 2010
Fort Lewis College Mathematics faculty and husband and wife Erich McAlister and Veronika Furst recently co-authored the paper, “Multiresolution Equivalence and Path-Connectedness,” appearing in the August 2014 issue of Numerical Functional Analysis and Optimization.
About the Paper
Drs. Furst and McAlister built upon the study of an equivalence relation between multiresolution analyses that was first introduced by Papadakis Stavropoulos and Kalouptsidis in 1996. An analogous definition in a generalized context was given by Baggett, Furst, Merrill, and Packer in 2010. The Fort Lewis College professors’ paper describes the relationship between these two notions and shows that both types of equivalence classes are path-connected in an operator-theoretic sense.
Dr. McAlister Explains the Paper
“Multiresolution analysis is the idea of breaking down data into varying levels of data. Path connectedness is about getting from point A to point B and the small differences that an object experiences from one step to the next. We showed how to connect two different ways of breaking down the same data in a predictable manner.”