Rice University
2015 — 2019Bachelor of Arts - Computer Science and Concentration in Business
Ever since I was young I have loved to build things. But my love for building machines that could help me solve problems started with the simple game Mancala. After sifting through Game Theory papers and my first Artificial Intelligence literature, I built a game-tree solver for that simple 2-player African board game. Further cementing my interest in artificially intelligent systems were projects predicting the 2019 NFL Hall of Fame awardees, and developing a computer vision app to solve the simple question of "Is this compostable or recyclable or neither?!"
Since these experiences, I've decided to build out more impactful artificially intelligent systems at scale. Most recently, I'm very interested in building on top of ₿itcoin to get this open financial network in the hands of those who need it most internationally. You may explore my formal work experience here on this site.
I've been learning efficient systems since I was 16 and purposely brush up on these daily.
Building out machine learning pipelines (Kafka, Spark, Scala) and infrastructure (ETL with Python, SQL, AWS Lambda).
Professional, fully OpenAPI compliant API development and application engineering in Java, Scala, and Python. Well versed with SQL and NoSQL document based databases.
I've built out productionized machine learning models for Capital One for real-time fraud detection, insider threat detection, command and control detection, and my latest project is NLP and violence mitigation.
Bachelor of Arts - Computer Science and Concentration in Business
Enrolled in Western Kentucky University Honors program at age 16. Here is where I initially found programming.
• Overhauled serial ETL (Extract, Transform, Load) processes into a concurrent design leading to miniscule computational footprint – optimized from 12 hours to 16 minutes AWS EC2 compute time (45x speed-up) $200,000/year saved
• Built Snowflake, SQS, Lambda, Kafka machine learning pipeline for real-time insider threat detection of AWS logs to combat. Utilized model selection to fit data with seasonal, trend, or serial models respective to each subset’s features
• Implemented Scala, Spark, AWS Redshift machine learning pipeline to predict real-time fraud
• Identified a flaw in data cleaning, increased the healthy data from 50% to 90% useable data (millions of records)
• Discovered OWASP Top 10 vulnerability for AWS serverless implementation – NPI over DNS Lambda exfiltration
• Explained beacon detection model and findings and security vulnerability to Senior Director
• Designed, implemented, & documented AWS Lambda driven entire REST API compliant with OpenAPI standards
• Translated engineering concepts & results into business outcomes for CEO
• Scoped engineering plans from business goals
• Designed & PoC’d system architecture: Firebase Cloudstore connected to Flutter/Dart for hybrid mobile development
• Enabled junior engineers by creating tutorials, NoSQL schemas, UI diagrams, and documentation for coding best practices
• Mentored 4 new grad engineers & delegated development by skillset.
• Led team in scrum meetings, sprint planning, and retrospectives. Interviewed technical candidates
• Reviewed code & implemented unit testing framework
• Built 700-2500 MHz cell receiver site with parabolic and LDPA antennas, optimized cellular modems (TTL, UID spoofing) with PoE copper ethernet backhaul, leading to a mesh node PTMP subnet distributing internet to rural community
• Boosted network performance ~20x (~1000ms, 8mbps, and 0.5mbps) to (~50ms ping, 130mbps, 50mbps)
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