Elizabeth Munch

Elizabeth Munch

Assistant Professor, Department of Computational Mathematics, Science and Engineering; Department of Mathematics;
Room 1511, Engineering Building
  428 S. Shaw Ln.
 (517) 432-0619
 muncheli@msu.edu

    About Me

    Education:

    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.

     

    Bio:

    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.

    After obtaining her B.A. in Mathematics from the University of Rochester, Liz continued her studies as a Graduate Research Assistant at Duke University where she got her M.A. and Ph.D in Mathematics. After earning her Ph.D Liz became a Visiting Assistant Professor at Duke until August 2013. From 2013 to 2014 Liz did her Postdoc at the Institute for Mathematics and Its Applications for the University of Minnesota. From 2014 to 2017, Liz was an Assistant Professor in the Department of Mathematics and Statistics at the University at Albany - SUNY.

    As of August 2017, Liz has joined the Department of Computational Mathematics, Science and Engineering and the Department of Mathematics at Michigan State University.

    Research Interests

    •    Applied Topology

    •    Topological Data Analysis.

    Postdoc Positions
    Munch Group

    We are working on building theoretical understanding and practical computational implementations of metrics for graph-like signatures arising in the field of Topological Data Analysis.  Specifically, the postdoc will be working on the interleaving distance for Reeb graphs and Mapper.  The successful applicant working with Dr. Munch will have a strong mathematical and/or theoretical computer science background, as well as comfort with programming and implementation, preferably in python.  http://elizabethmunch.com/math/
     

    Selected Publications
    [1]
    Paul Bendich, Sang Peter Chin, Jesse Clark, Jonathan Desena, John Harer, Elizabeth Munch, Andrew Newman, David Porter, David Rouse, Nate Strawn, and Adam Watkins. “Topological & Statistical Behavior Classifiers for Tracking Applications”. In: IEEE Transactions on Aerospace and Electronic Systems 52.6 (2016), pp. 2644–2661. doi: 10 . 1109 / taes . 2016 . 160405. url: http://ieeexplore.ieee.org/document/7855573/
     
    [2]
    Vin de Silva, Elizabeth Munch, and Amit Patel. “Categorified Reeb Graphs”. In: Discrete & Computational Geometry (2016), pp. 1–53. issn: 1432-0444. doi: 10.1007/s00454-016-9763-9. url: http://link.springer.com/article/10.1007/s00454-016-9763-9.
     
    [3]
    Firas A. Khasawneh and Elizabeth Munch. “Chatter Detection in Turning using Persistent Homology”. In: Mechanical Systems and Signal Processing 70-71 (2016), pp. 527–541. issn: 0888-3270. doi: 10 . 1016 / j . ymssp . 2015 . 09 . 046. url: http://www.sciencedirect.com/science/article/pii/S0888327015004598.
     
    [4]
    Elizabeth Munch, Katharine Turner, Paul Bendich, Sayan Mukherjee, Jonathan Mattingly, and John Harer. “Probabilistic Fr´echet means for Time Varying Persistence Diagrams”. In: Electron. J. Statist. 9 (2015), pp. 1173–1204. doi: 10.1214/15-EJS1030. url: https://projecteuclid.org/euclid.ejs/1433195858.
     
    [5]
    Elizabeth Munch, Michael Shapiro, and John Harer. “Failure Filtrations for Fenced Sensor Networks”. In: The International Journal of Robotics Research 31.9 (2012), pp. 1044–1056. doi: 10 . 1177 / 0278364912451671. url: http://journals.sagepub.com/doi/abs/10.1177/0278364912451671.

    ...

    Teaching

    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.