About Me


Professional Experience

  • Software Engineer - Google (May 2022 -  Present)

  • R&D Computer & Data Scientist - Sandia National Laboratories (June 2020 -  May 2022)
    • Machine learning on TB scale using multi-modal data in a research environment
    • Complex problem solving by researching, architecting, & implementing creative solutions to big data problems
    • Point of contact for two projects with 7 direct reporters & a $410K combined FY21 allocation
    • Demonstrates leadership by interviewing, onboarding, & mentoring talented MOWs to enable optimal results
  • Graduate Research Assistant - Auburn University (May 2018 - May 2020)
    Research into Exploratory Modeling using NetLogo, Scala and Python for sustainable Wargaming logistics for the US Air Force and research on Coherent Agent simulations where I designed and implemented an API for Agents to interact on contexts.

  • Graduate Teaching Assistant - Auburn University (May 2019 -  August 2019)
    Teaching Assistant for an Introduction to Algorithms class of 36 students. My responsibilities included grading, proctoring, holding office hours and study sessions.

  • Software Engineer - WonUpIt (January 2015 - August 2017)
    At WonUpIt I gained valuable experience with data-driven applications. My responsibilities included overseeing app architecture, improving the User Interface and maintaining legacy code. In addition, I managed a team of developers to release more than 10 versions of the WonUpIt app.

  • Technician - Everwave Technologies (May 2016 - August 2016)
    As a Technician I received hands on experience with the first three layers of Internet Protocol. I learned ISP technologies such as Packet Switching, installation of T1 lines and Y-Max high power radio beaming to bring fast and reliable internet access to the Rocky Mountains.

  • iOS Developer & Tutor - Brodderick.com (2010 - 2018)
    I began as a self-taught programmer working on individual projects. I released several apps to the Apple App Store and eventually began contract work. The apps I created on my own include two games and an app that makes it easy to track how much you are spending on gasoline. In addition, I tutored iOS development to young students who were eager to create.


  • Strategy Learning System - (2019 - 2020) (GitHub) (PowerPoint)
    An architecture developed in cooperation with a U.S. Air Force contract for wargaming policy analysis. The system uses a model ensemble method coupled with rule-based machine learning to discover how to yield desirable results from a wargaming simulation.

  • Hypertune - (2019) (GitHub)
    Hyperparameter tuning using Particle Swarm Optimization and parallel computation which outperforms current approaches.

  • Cassowary (collaboration) - (2020 - Present) (GitHub) (homepage)
    Took over maintaining the cassowary constraint satisfaction repository.

  • XCSR - (2019) (GitHub)
    A rule-based classifier from Wilson’s accuracy-based Learning Classifier System. Expands the traditional approach to include multiclass and real-values.

  • XCS - (2019) (GitHub)
    A rule-based classifier from Wilson’s accuracy-based Learning Classifier System.

  • Shut The Box Reinforcement Learning Agent - (2019) (GitHub)
    A Q-Learning agent to play the game of Shut the Box.

  • Texas Hold’em Reinforcement Learning Agent (collaboration) - (2019) (GitHub)
    An implementation of DQN and Monte Carlo Tree Search to play the game of Texas Hold'em.

  • Amino Acid ID3 - (2019) (GitHub)
    An implementation of the ID3 Decision Tree algorithm using an amino acid dataset.

  • Contaminant Plume Model - (2018) (GitHub)
    Extends the Madey, Wilensky Contaminant Plume Model by using/refining the strategies presented in the multi-agent coordination paper with a specific focus in identifying variation points and variability management.

  • Mountain Car Reinforcement Learning Agent - (2018 - Present) (GitHub)
    Q-Learning & SARSA implementation to beat the OpenAI Mountain Car environment.

  • Approximating the Spectrum of a Graph (collaboration) - (2018) (GitHub)
    An implementation of the Cohen-Steiner, Et. al. KDD ’18 paper to approximate the spectrum of a graph.

  • A* Path Finding - (2018) (GitHub)
    An implementation of the A* (shortest path) algorithm to solve the misplaced tiles problem.

  • Coherent Agent Visualization Tool - (2018) (GitHub)
    An API to visualize how Coherent Agents (Cogents) interact with one another on Grid, Network, or 2D-Space contexts.

  • Interactive Handwriting (collaboration) - (2018) (GitHub)
    An Android app which used Bluetooth and WiFi to allow multiple users to collaborate on hand-written documents.

  • XOR Neural Network in C++ - (2017) (GitHub)
    A-from-scratch implementation of a Neural Network using Stochastic Gradient Decent to predict the outcome of a boolean XOR comparison.


  • M.S. Computer Science, Machine Learning - Auburn University (May 2020)

  • B.S. Computer Science - Auburn University (December 2018)

  • Relevant Coursework: Artificial Intelligence, Machine Learning, Adversarial Machine Learning, Computational Biology, Deep Learning, Data Mining, Algorithms & Data Structures, Software Modeling & Design