I am 24 with 12 years of programming experience.
I continue to grow my skillset with each problem I tackle.
I got started when I built and sold iOS games on the Apple App Store.
I am now aspiring to be a Machine Learning Engineer.
- 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.
- 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 - Present) (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