Slides and code from a talk at the Boston Bayesians meeetup on 15 June, 2017.
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.”
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 command line utility for automatically compiling $\LaTeX$, and eliminating auxiliary files. Try it out with
pip install tidytex.
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.
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.
PhD in Mathematics, 2012
MA in Mathematics and Economics, 2007