You can find most of my work on either GitHub, Instructables, or /r/DataIsBeautiful.

Please feel free to leave a comment below, I’d love to have your feedback!



I am a regular contributor to /r/DataIsBeautiful, a data visualization community on Reddit. A few of my posts have been quite popular on the site, collectively receiving more than a million views. You can take a look at the list of all of my posts or check out one of the popular ones below:

SportradarAPIsSummer 2018


Sportradar is a company that provides extensive APIs for a number of professional sports including soccer, basketball, and multiple eSports leagues. I wrote a Python package that makes it easy for users to download data from Sportradar’s APIs. A fun part of the project was writing a script to scrape the Sportradar documentation and automatically generate functional Python code and unit tests for each API endpoint. My post on data from the 2018 FIFA World Cup provides an example of how to download data using the package.

LyricsGeniusFall 2017


LyricsGenius is a Python package I wrote that allows users to programmatically access lyrics, artist information, and song media from Genius is a fun website that let users upload song lyrics and collaborate on annotations and interpretations of artists’ words. I wanted a simple way to download lots of song lyrics at once, so I thought I’d write a Python wrapper for the API Genius provides. The code is hosted on GitHub and can be installed using from PyPI. I used LyricsGenius for my analysis of country song lyrics.

FivecircleFall 2017


Fivecircle is a geo-based social media platform I built with a five-person team as part of a software engineering course I took in the fall of 2017. The web app lets users post geo-tagged photos and notes for other users to view. We built Fivecircle using Ruby on Rails while following an Agile methodology and maintaining best practices on our GitHub repository. We made use of the Google Maps API for geocoding as well as the devise ruby gem for authentication. Sign up for an account and start sharing posts!

Active shape models for face detectionSpring 2017


For the final project in my Advanced Digital Image Processing course, I chose to research and implement active shape models (ASMs), a technique originally developed by Tim Cootes et al. in 1995. My Matlab code determines a point distribution model from a manually-labeled face training set and is then able to successfully locate faces in new images using the ASM technique. Check out the GitHub repository for a thorough description of the project.

EMG audio amplifierSpring 2017


During the spring of 2017 I built an electromyography (EMG) audio amplifier. The two-channel device was built from analog integrated circuit components (op-amps, instrumentation amps, and audio amps), included band-pass filters, and could output audio through a standard 1/8” audio jack. Visit the link for images and schematics of the device as well as detailed instructions for building your own. After I published my project to the Instructables website, the HackADay blog featured my work and Tweeted a link out to their 100K+ followers!

Muscle-controlled MIDI deviceWinter 2017


During the winter of 2017, I built an electromyography (EMG) amplifier which allowed users to trigger MIDI instrument sounds (e.g. a snare drum) by flexing their muscles. The device used muscle activity as a control signal for both volume and pitch of the MIDI notes. After the muscle activity was amplified and filtered via a custom-built analog circuit, an Arduino translated the EMG signals into MIDI signals which were then sent over Bluetooth to an iPhone running Garageband. I ended up winning a prize in the Instructables Sensors 2017 contest.

Halloween drum machine costumeFall 2016


For Halloween 2016 I designed and built a MIDI drum machine costume. The costume consisted of piezoelectric sensors worn on the chest which triggered MIDI drum machine noises played through an iPhone after the raw signals were processed on an Arduino. It was pretty fun.

Musician MakerSummer 2011


During the summer of 2011 I worked on the “Musician Maker” project as part of Goshen College’s Maple Scholars Program. Musician Maker is an intuitive, computer-controlled system of novel instruments that allows non-musicians to improvise expressive music. During the winter of 2012 my advisor, John Buschert, and I competed and were selected as finalists in the 2012 Guthman New Musical Instrument Competition hosted at Georgia Tech. I wrote multi-threaded Python code to generate the artificial music and interface with the novel hardware instruments.

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John W. Miller © 2018
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