Data scientist and machine learning engineer. PhD in Computer Science. Ten years building ML that runs in production, based in Atlanta.
Most of my career has been the part of machine learning that gets hard after the demo: real-time inference on live data, where latency, throughput, and reliability under changing conditions matter more than another point on a benchmark. Most recently I led a data science team shipping detection, classification, tracking, and segmentation into production.
For the past two years I ran an independent quantitative research program. I built the data pipelines, the options analytics, and a tick-level backtesting and validation framework, then used it to test thirteen strategies and reject twelve. The work I am proudest of there is not a strategy. It is the methodology: null controls, out-of-sample discipline, and catching the ways a backtest lies to you before you trade it.
Earlier: a postdoctoral fellowship at the U.S. Army Research Laboratory (behavior classification, motion prediction), and a PhD at the University of Georgia in spatial place recognition for mobile robots.