• Currently a Postdoctoral Fellow in Mathematics at Colorado State University
  • PhD in Mathematics from University of North Carolina, Chapel Hill
  • BS in Mathematics from University of Colorado Denver
  • I'm from Colorado! (Everyone seems to have Colorado pride nowadays.)
  • Aside from math, I enjoy biking, hiking, website design (check out the collapsible sections on this page!), making vector graphics, playing video games, and some other things too.
  • Email:  
I'm not teaching during academic year 2017, but I have plenty of experience teaching and writing curriculum for a wide range of courses; click the link above for more details.
These are some of my current and past research interests and projects.
  • Passive tracer problems. My PhD research is on transport of passive scalars in shear flow, working with Roberto Camassa and Rich McLaughlin. The broad research question is essentially, "when a dye gets pushed by a fluid flow in a pipe, what influence does the shape of the pipe have in shaping the dye?" Our approach is to both apply techniques in mathematical analysis to the advection-diffusion equation to gain quantitative predictions at both short and long times, and use numerical simulations for validatation. The short answer is that the cross section does play an important role. We have published papers at both Physical Review Letters and Science covering different aspects of this question.
  • Stability problems. I spent the summer of 2013 at Los Alamos National Lab, working with Balu Nadiga on a stability problem in geophysical fluid dynamics. The broad goal here is to be able to understand the mechanism for, and size of, instabilities in ocean flows. My project was to develop a numerical code for one such scenario.
  • Spectral image segmentation. In my undergraduate, I worked with Andrew Knyazev on a project on spectral image segmentation. The goal is to develop a black-box method for identifying the salient features of an image. I also briefly assisted on a code for optimal polynomial fitting in the sup-norm, that is, finding an optimal n-th degree polynomial to minimize the sup-norm of the error function f(x) - pn(x) on an interval.