Software Engineer & Founder
I'm a full stack software engineer drawn to hard problems that improve lives. Finding structure in ambiguity, experimenting, and iterating rapidly are my love languages.
Previously worked in Search, Data Infrastructure, and Networking at Google. Currently building Twined at the intersection of AI and human communication.
I live in the San Francisco Bay Area with my wife, Hayley, and our two dogs, Bella and Brownie.
My path here wasn't smooth. Hurricane Katrina destroyed my childhood home and possessions, forcing my family to relocate from New Orleans to Nashville. I faced bullying at school and shaken stability at home. I developed a strong empathy for outliers and learned to grow amidst adversity. At 15, I was accepted to Lipscomb University. I dual-enrolled through high school, then went on to study Computer Science at Texas A&M. Google recruited me out of school, and I spent nearly six years building frameworks for the Search Results Page, distributed systems for Health Data, and testing infrastructure for gRPC.
Now I'm working on something of my own, driven by the same resilience, curiosity, and sense of responsibility forged in those early years.
Twined
Founded Twined Inc. and re-designed messaging from the ground up. Built and deployed the iOS application, backend services, and ML infrastructure. Built data pipelines and trained task-specific models with custom architectures. Pitched to a VC, and currently preparing to launch publicly.
Built frameworks that power the Search Results Page, improving performance and developer productivity. Developed distributed systems for Health Data Pipeline orchestration. Designed infrastructure with custom Kubernetes controllers to test gRPC on Google Cloud.
Texas Instruments
Built monitoring software for job orchestration systems.
IBM
Integrated de-identification and pseudo-anonymization for a big data pipeline.
Faithlife
Worked on RefTagger (embedded Bible references across the web) and backend services.
Lipscomb University
Taught programming and robotics to children.
Reimagining digital communication with AI, making it more context-aware, efficient, and useful.
Entity recognition, intent detection, utterance prediction, coreference resolution, thematic hierarchies and disentanglement.
Encoder architectures, contrastive objectives, curriculum learning, semi-supervised learning, adversarial networks, and reinforcement learning.
Dense & sparse vector representations, vector stores, and retrieval-augmented generation.
User agency, AI ethics, transparency, feedback loops, data de-identification and differential privacy.
Model routing, selective tool use, task planning, grounded search, and evaluation.
On-device inference, energy-aware computation, and device/network–aware model cascades.