Before joining ICER, Craig earned a Ph.D. in applied mathematics from Michigan State University where his research focused on developing fast, provably correct algorithms for high-dimensional function approximation and solving partial differential equations.
Craig's research interests lie broadly at the intersection of mathematical data analysis and computationally difficult scientific problems. You may find him developing data pipelines in the Tidyverse one day and prototyping robust solvers in Python or MATLAB the next.
At ICER, Craig's primary goal is to increase access to research computing across disciplines. He enjoys helping researchers find approachable tools that can complement and accelerate their workflows.