Who am I?

Greetings! I'm Zach, and I love digging into deep questions using data analysis techniques. I started my career by getting a Ph.D. in Physics and applying my love of statistics and coding to the "deep problems of the universe." I've run experiments that studied how nuclei split apart to release energy (nuclear fission), what makes up a nucleus, and numerous other physics topics. All of these tasks shared two common themes: how do we get tons of data and what do we do with it once we have it? Solving these problems was the key to unlocking the answers to the puzzles of the universe. However, for me, what always mattered the most was problem solving through data techniques. So, yes, I've loved working with huge collaborations to study what happened in the first nanoseconds after the Big Bang... but I'm just as excited to use analysis techniques to predict what soup someone will eat based on their lifestyle! I'm fluent in C++, Python and UNIX, and I have a working knowledge of R, Java, BASH, LaTeX, C#, and Javascript. I'm also a musician, a rock climber, and have a deep love of green chile. See More...

Featured Projects:

Compendium Of Talks

Compendium of Talks

Want to hear me talk about data science? That's a thing I do sometimes.
Tuple vs List Speed

Are Tuples Faster than Lists?

In Python, the common idea is that tuples are faster than lists for everyday needs like accessing data. I thought that sounded strange, so I spent way too much time testing it.
Simple Recommendation Engines

Recommendation Engines for Dummies

A look into how collaborative filtering works for recommendations, with some Python code to build your own from scratch. Targeted for those without deep techincal knowledge of data science.
Analysis Tree Maker

Analysis Tree Maker

This C++ code is used to convert an unmanageable 60+TB of data into a smaller, but still usable data structure for extracting physics results.
Random Walker Example

The Drunken Walker(s)

A visualization of the classic "drunken walk" physics thought-experiment. A mix of random numbers and Jackson Pollock.