Bio


David Gunderman is a visiting postdoctoral scholar in the lab of Alison Marsden studying numerical methods for cardiovascular modelling and simulation. He is also a Lillian Gilbreth Postdoctoral Fellow at Purdue University. His work focuses on developing deep-learning methods for automatic extraction of cardiovascular structures from image data and applying isogeometric analysis to improve the efficiency and robustness of cardiovascular simulations.

David received his AB summa cum laude in Mathematics and German from Wabash College, completed a Fulbright Fellowship in Germany, and then earned his PhD from the University of Colorado Boulder, where he studied computational geometry, statistical collaboration, and algorithms for numerical PDEs under advisers Bengt Fornberg and John Evans. While at CU Boulder, he worked as a High Energy Density Physics Research Fellow at Lawrence Livermore National Laboratory, as a statistical consultant for the Laboratory for Interdisciplinary Statistical Analysis, and as an instructor in the Department of Applied Mathematics.