Ph.D. in Mathematics, Duke University, 2013.
M.A. in Mathematics, Duke University, 2010.
B.S. in Mathematics, University of Rochester, 2008.
B.M. in Harp Performance, Univeristy of Rochester, 2008.
Liz received her PhD from the Department of Mathematics at Duke University in May 2013. She was a Postdoctoral Fellow at the Institute for Mathematics and its Applications at the University of Minnesota for the 2013-2014 thematic year on applications of topology. She also holds a Master of Arts in Mathematics from Duke University, a Bachelor of Science in Mathematics from the University of Rochester, and a Bachelor of Music in Harp Performance from the Eastman School of Music. Prior to joining CMSE, Liz was an Assistant Professor in the Department of Mathematics and Statistics at the University at Albany - SUNY from 2014-2017.
As of August 2017, Liz has joined the Department of Computational Mathematics, Science and Engineering and the Department of Mathematics at Michigan State University.
• Applied Topology
• Topological Data Analysis.
Recently recieved a NSF grant:
The labs of Dr. Elizabeth Munch (http://elizabethmunch.com/) and Dr. Dan Chitwood (https://dhchitwood.wixsite.com/morphologylab) in the Department of Computational Mathematics, Science & Engineering at Michigan State University invite applications for a Research Associate-Fixed Term (Postdoctoral Researcher). The successful candidate will have previous experience in applying Topological Data Analysis (TDA) to scientific questions and working productively with mathematicians and computer scientists, as well as domain scientists without mathematical training. A focus of the research the candidate will undertake will be using TDA to quantify 3D, voxel-based images collected using X-ray Computed Tomography. The research team the candidate will join focuses on applying TDA to models of plant morphology, but the candidate will also work broadly across disparate fields of science, and a collaborative, diplomatic, team-oriented personality is a prerequisite for this position. Enthusiasm for education and outreach is a must, as in addition to publishing results, the candidate will disseminate TDA methods, code, data, and resources they develop to the scientific community in a reproducible, open, and innovative way. Above all, the candidate will have a passion for applying TDA approaches in the sciences and working with scientists in a productive manner to arrive at a working theory of applied topology in the sciences.
Review of applications will begin on December 16, 2019, and will continue until the position has been filled.
FS17: CMSE 491 Topological Analysis of Large Datasets
FS17: CMSE 890 Topological Analysis of Large Datasets
SS18: CMSE 201 Intro Computational Modeling
Click "Teaching" link to see past courses.