My setup for running multiple versions of Python
Slides and code from a talk at the Boston Bayesians meeetup on 15 June, 2017.
A web app for generating samples from a sketched probability distribution function.
I am a contributor to PyMC3, a “Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.”
An offshoot of another project, this allows you to compare times between most collegiate cross country courses.
A demonstration of using Flask, React, and d3js to visualize machine learning models. This is a port of a previous project from Angular to React.
Using hundreds of thousands of historical cross country running results to make predictions about future meets. The page is updated more than weekly during the season.
A small project which once a day checks for new words on Futility Closet and keeps a searchable, downloadable list of the curious words it finds there. Click the random button a few times, or make an Anki study deck.
For the second year in a row, I took part in Kaggle’s contest to predict March Madness winners. The code for the actual model is not very expository (get in touch if you are interested), but I also built a friendlier page to query predictions interactively at the link.
An interactive page for demonstrating and experimenting with the beta distribution. Built with AngularJS and d3js.
A command line utility for automatically compiling $\LaTeX$, and eliminating auxiliary files. Try it out with pip install tidytex
.
A small script built on top of d3js allowing you to define simple animated parametric equations.
A demonstration of linear regression, overfitting, normalization, and regularization. Allows you to choose data from a distribution, and interactively fit polynomials to the data using least squares, ridge, or Lasso regression.
Thu, Jun 15, 2017, Boston Bayesians Meetup
Sat, Sep 17, 2016, Kensho Machine Learning Seminar
Fri, May 13, 2016, Phillips Academy Bayesian Stats course
I am a software engineer and mathematician in Cambridge, MA, working on machine learning and natural language processing. I am a contributor to the popular PyMC3 library. I received my doctorate studying geometric measure theory with Dr. Robert Hardt at Rice University.
In my spare time I run, walk in the woods with Pete the pup, and launch balloons into [near] space.
PhD in Mathematics, 2012
Rice University
MA in Mathematics and Economics, 2007
Williams College