What's in a Like?

MathematElection is a computational politics blog that uses Like data from Facebook to predict Election 2016. My goal is to provide exploratory data analysis and (admittedly lighthearted) predictions, delivering insights from data to a broad audience. So if you want to know who our next president will be, want to learn some coding or statistics, or even become the next Nate Silver, this is the blog for you!

In addition to the creating the blog itself, I've presented on this project at the 2015 Wolfram Technology Conference, as Wolfram technologies comprise the computational backbone for my work. The code is available on GitHub. My presentation may be found here (PDF), here (Mathematica Notebook), and on YouTube:

Over the past 30 years, an average of 85 people died each year in the US due to flash-floods, making them the most fatal severe weather condition. Particularly in Central Texas, the "most flash-flood prone area in the United States," we need to accurately predict rainfall. However, meteorologists continue to manually adjust state-of-the-art physical models based on experience, rather than objective methods.

This is where I come in.

My undergraduate thesis project uses neural networks and conditional random fields to better estimate rainfall in the Central Texas area. Rather than making future predictions, my project aims at determining the precise relationship between Doppler radar data and rainfall on the ground. For more details, be sure to check out the full write-up here and the code on GitHub. Special thanks goes to Dr. Michael Marder and Dr. Pradeep Ravikumar who supervised my research.

Data is everything. Even for physics. Especially for physics.

Physicists (both praciticing and in training) rely on data for insight into the laws of the universe. However, processing data effectively, analyzing data correctly, and presenting data elegantly are skills often learned on an ad hoc basis, rather than in a classroom setting.

Will Beason and I set out to correct that.

Will and I co-wrote Data Analysis for Physics as an online introductory textbook using Brad Miller's runestone system. In it, we cover the basics of using Wolfram Mathematica, LaTeX, and basic statistics as a bare-bones introduction to professional physics (really, professional research). Though many of the examples come from a physics perspective, the techniques are far more general.

The course materials (available at eaott.github.io/data-analysis) have been used for multiple 1-hour, for-credit, student-led classes at UT-Austin since Will and I co-led the first iteration in Spring 2014. I'm especially thankful to Dr. Greg Sitz who handed us the reins for the curriculum in a grand educational experiment.