About me:

This was previously my personal webpage as a grad student at University of California, Irvine. While there, I studied applications of multiscale modeling techniques to machine learning under the guidance of my advisor, Eric Mjolsness. I have since graduated and moved on to a tenure-track position at Colorado College, a small liberal arts college in Colorado Springs. My personal webpage at CC is more up-to-date about my current research and service pursuits. This page now mainly serves as a respository for my PhD thesis work.

Thesis pages:

In order, here is a short description of the chapters of my thesis, all available on this site.
Note: the HTML pages on this website were generated by an automated tool from my original LaTeX files. This process is brittle and prone to error, so these pages are presented here for convenience and should be considered inferior to the `canonical' version, a PDF available here In the case of any inconsistency (in particular, buggy equations or figures) the PDF wins out.

1. Introduction What makes GDD (Graph Diffusion Distance) interesting to study? What are some similar other approaches for comparing graphs?
2. Diffusion Distance - Definitions
3. Diffusion Distance - Properties
4. Diffusion Distance - Efficent Calculation
5. Diffusion Distance - Numerical Properties
6. Multiscale Neural Network Training
7. Graph Prolongation Convolutional Networks
8. Other Applications
9. Conclusion and References

Other things I'm passionate about: teaching, PoC and LGBT+ inclusion in the sciences, and making things! Feel free to email me to discuss research or any of these topics.

"I am, somehow, less interested in the weight and convolutions of Einstein's brain than in the near certainty that people of equal talent have lived and died in cotton fields and sweatshops." - Steven J Gould