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.
Can you beat Video Poker?

Monte Carlo (Pt 5), Monte Carlo and Business

The fifth in a series of posts dedicated to Monte Carlo. In this edition, we apply MC to see how a choice changes our business's bottom line.
Can you beat Video Poker?

Monte Carlo (Pt 4), Let's Simulate Particles

The fourth in a series of posts dedicated to Monte Carlo. In this edition, we build a 1D particle simulator.
Can you beat Video Poker?

Monte Carlo (Pt 3), Can you beat Video Poker?

The third in a series of posts dedicated to Monte Carlo. This time, we try to outsmart the casino by learning to play video poker as optimally as possible.
How do we Monte Carlo with Python?

Monte Carlo (Part 2), Monte Carlo + Python?

The second in a series of posts dedicated to Monte Carlo. This time, we solve a few simple problems with Python, to learn how Monte Carlo's work under the hood.
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.