Senior ML Engineer and data scientist specializing in production ML, probabilistic modeling, real-time inference, LLM systems, and MLOps. I've shipped novel algorithms from research to production, built distributed data platforms, and led end-to-end ML across high-stakes environments.
My work sits at the intersection of deep technical ML and the engineering discipline required to ship it reliably. Building in high-stakes environments forces real rigor: reproducible experiments, measurable outcomes, systems that hold up under adversarial conditions.
I believe the most trustworthy ML systems are built on probabilistic thinking, careful instrumentation, and the willingness to treat every deployment as a live experiment worth measuring.
EpiWatch
Led development of a wearable AI platform from idea to market-ready medical device.
Orba
Transforming ambitious technical concepts into commercial products.
JHU BCI Lab · Johns Hopkins University
Production real-time inference pipeline, neural decoding with HMMs and deep learning, and LLM-based BCI with long-term memory.
I write about probabilistic modeling in practice, MLOps patterns for real-world systems, LLM system design, and the engineering discipline required to ship reliable ML.
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Dallas, TX
Technology alone doesn't build a successful venture. I am deeply engaged in the entire go-to-market lifecycle, from product-market fit to commercialization strategy.
Outside of work, I am an avid runner and tennis player. I used to play high-level competitive cricket and soccer. In the winters, you'll find me skiing or ice-skating.
Currently based in Dallas, TX.