I graduated from the University of California, San Diego in June 2018 with a degree in Pure Mathematics and a minor in Computer Science. I currently work as a Data Scientist, primarily doing forecasting and mathematical modeling, and am planning to begin pursuing a Ph.D in Mathematics in 2019.
I am broadly interested in algebraic aspects of geometry and topology – I participated in undergraduate research in Topology with the inimitable Justin Roberts, which included a study of spectral sequences, homotopy theory, fiber bundles, classifying spaces, characteristic classes, and basic intersection theory.
You can find details concerning my experience and educational background at the links at the top of the page. Under “resources”, I’ve assembled some math-related advice, recommendations, and answers to questions that I often hear students ask.
You can also find my blog posts here – I generally find it’s easiest to learn material when I attempt to write it up, so these posts are my efforts to convert sketches, notes, and outlines into something readable. I welcome any feedback or suggestions anyone might have – feel free to contact me via email, and my direct/personal messages on social media are always open!
Links and Projects
- I host a fork of the Arxiv Sanity Preserver , which regularly indexes papers from a number of Mathematics categories on Arxiv.com.
- A gallery of math-related images and diagrams I’ve created for various talks and papers, as well as various other photos.
- An animated visualization of persistent homology on a time-evolving simplicial complex. (Work in Progress!)
- A square topology toy in HTML/Canvas, designed to visualize walking around and drawing graphs in spaces with topologies arising from identifying the edges of a square.
- A giant diagram of algebraic structures
- My (rather unorganized!) directory of various notes
A relatively short introduction to Category Theory, including some concrete examples.
Notes on some nifty packages and tools for Haskell development.
Implementing some ‘Stats 101’ ideas in R.
Determining the running-time complexity of some common types of functions.