Hailing from Denver, Colorado, I am an engineer-turned-data scientist who loves to explore the unknown and discover new ways to compete. As a husband and a proud father, I cherish building deep bonds with my family and having the privilege to guide my children through this beautiful and complex world alongside an incredible partner. Each day brings new opportunity; I aim to be present for every moment I am given.
I am a technical professional drawn to extraordinary people conducting ambitious & audacious work. Naturally curious, I look for the underlying interconnections that hide gems of opportunity, and bring to bear modern technology wherever I can. In that spirit, I now aim to inform better strategic and business decisions with the application of Machine Learning, Statistics, and Software, deploying experience and creativity at all levels of the organizations I serve.
With over a decade as a trusted partner within aerospace engineering teams producing communication systems and satellites, I have a track record of delivering innovative solutions under stringent and demanding conditions. I have built an award winning mini-baja race vehicle, airplanes out of carbon fiber, overseen the mechanical needs of multiple large anechoic test chamber facilities, and designed systems for the assembly, integration, test, and handling of multimillion dollar satellite systems. As of Spring 2022, I have completed a full-time intensive data science course, through General Assembly, with the particular intention of leveraging new skills in statistical learning to drive higher performance into scientific and business environments.
Here are a few of my latest projects in the data science space.
Computer generated images have made astonishing leaps in quality over the past few years; so much so, that human beings struggle to outperform random chance in identifying whether an image is fabricated or real.
I wanted to see if it was possible to produce a Convolutional Neural Net (CNN) that could outperform people at this challenging task.
The COVID epidemic has exacted an extraordinary toll globally, and while the world slowly begins to return to normal in terms of health safety, the economic impacts continue to be felt for the nations of the world. This made our team wonder: are there particular types of economies that weather economic disruption greater than others? We used unsupervised machine learning models to try and answer that question for ourselves.
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This project demonstrates the use of the Pushshift API and Natural Language Processing to generate a model that can accurately determine (with over 96% accuracy) where a particular piece of content originated (in this case, a particular subreddit).
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In this project I create a home price estimator, similar to popular websites like Zillow or Redfin, specifically for the Ames Iowa housing market based on the Kaggle dataset by the same name. I have two final models: one is performance based, accounting for 84% of the variance relative to the null hypothesis, and the other is a simplified model with a focus on interpretability.
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