About Me

Welcome! I currently work at the National Geospatial-Intelligence Agency (NGA) as the Data Lead for the overhead satellite component of a large computer vision program. I specialize in transforming complex datasets into actionable insights through cross-functional collaboration and innovative solutions. With extensive experience in data strategy, management, analysis, and science, I'm passionate about leveraging data to drive informed decision-making and leading technical discussions across various domains and applications.
I excel in breaking down complex problems and building solutions that have immediate actionable insights and recommendations. My day-to-day consists of leading multiple cross-functional technincal teams in the acquisition, pre-processing, curation, and labeling of petabytes of high-quality training data for model development. This includes managing complex data pipelines, setting data priortizations, developing data team strategies, and conducting in-depth data analysis to ensure the highest quality data in support of state-of-the-art deep learning model development for national security objectives through geospatial intelligence.
I served in the United States Navy for eight years, with three overseas deployments as an intelligence analyst. This invaluable experience instilled a strong work ethic, attention to detail, and the ability to thrive in high-pressure environments. It's also where I got my start in data, working with complex datasets to provide actionable intelligence to commanders in the field. This is where I honed my analytical skills and learned the importance of clear, succint, timely, and most importantly, accurate communication of complex information to diverse audiences.
Education is a passion of mine, whether for myself or teaching others. I'm finishing my second Bachelor of Science degree in data science (graduating December 2026) with focuses on machine learning, statistical analysis, and data visualization. My personal projects includes full-stack and data application development; in fact, this website was built from scratch using Next.js (React), Tailwind CSS, and a postgreSQL database. After graduation, I plan to pursue a Master's of Science in Data Analytics Engineering to further deepen my expertise in engineering and analysis.
What I'm Reading

Storytelling with Data: A Data Visualization Guide for Business Professionals
Cole Nussbaumer Knaflic

Midnight in Chernobyl: The Untold Story of the World's Greatest Nuclear Disaster
Adam Higginbotham

Naked Statistics: Stripping the Dread from the Data
Charles Wheelan

Why Machines Learn: The Elegant Math Behind Modern AI
Anil Ananthaswamy
Up Next

The Design of Web APIs, Second Edition
Arnaud Lauret

Probably Overthinking It: How to Use Data to Answer Questions, Avoid Statistical Traps, and Make Better Decisions
Allen B. Downey

The Nuclear Age: An Epic Race for Arms, Power, and Survival
Serhii Plokhy

The Signal and the Noise: Why So Many Predictions Fail--but Some Don't
Nate Silver

Real-World Machine Learning
Henrik Brink, Joseph Richards, Mark Fetherolf

Fundamentals of Data Engineering: Plan and Build Robust Data Systems
Joe Reis, Matt Housley

The Missing README: A Guide for the New Software Engineer
Chris Riccomini, Dmitriy Ryaboy

Prisoners of Geography: Ten Maps That Explain Everything About the World
Tim Marshall

Algorithms
Panos Louridas

How Charts Lie: Getting Smarter about Visual Information
Alberto Cairo

The Art of Statistics: How to Learn from Data
David Spiegelhalter

Practical Deep Learning, 2nd Edition
Ronald T. Kneusel

Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples
Jeremy Kubica

Chernobyl Roulette: War in the Nuclear Disaster Zone
Serhii Plokhy

Math for Deep Learning: What You Need to Know to Understand Neural Networks
Jeremy Kubica

Dive Into Algorithms: A Pythonic Adventure for the Intrepid Beginner
Bradford Tuckfield

Bayesian Statistics the Fun Way
Will Kurt

The Great Heist: China's Epic Campaign to Steal America's Secrets
David R. Shedd, Andrew Badger
Deeper Knowledge

Deep Learning
Goodfellow, Bengio, Courville

Deep Learning: Foundations and Concepts
Christopher M. Bishop, Hugh Bishop

Foundations of Computer Vision
Antonio Torralba, Phillip Isola

Theory of Spatial Statistics: A Concise Introduction
M.N.M. van Lieshout

Statistical Rethinking: A Bayesian Course with Examples in R and STAN
Richard McElreath

Deep Learning for Vision Systems
Mohamed Elgendy

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
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How Not to Be Wrong: The Power of Mathematical Thinking
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The Complete Developer
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Red Scarf Girl: A Memoir of the Cultural Revolution
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Doing Data Science: Straight Talk from the Frontline
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Things Fall Apart
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Educated: A Memoir
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How to Lie with Statistics
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Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
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How AI Works: From Sorcery to Science
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