Research-grade engineering, built to be handed off

I lead software engineering and research computing at the Harvard T.H. Chan School of Public Health and the Broad Institute of MIT and Harvard, where I've spent years building the systems that scientific research actually runs on.

That means maintaining HPC blades and shared cluster partitions, engineering 500TB+ of tiered storage, and building the pipelines, portals, and dashboards researchers depend on every day. I've shipped microbial community profiling tools to a global user base and automated training environments for hundreds of learners at once.

I develop and maintain state-of-the-art tools in the bioBakery suite for microbiome and multi-omics analysis, working shoulder-to-shoulder with biologists, postdocs, and PhD researchers to turn scientific ideas into reliable, reproducible software.

I've distributed research tools through GitHub, PyPI, Conda, and Docker with automated CI/CD and cross-platform builds, deployed the Galaxy platform for genomic and proteomic analysis, and built controlled-access data portals serving datasets at the multi-terabyte scale. My work also appears in peer-reviewed publications in eLife and other venues.

Before research computing, I built and scaled production systems in industry — developer platforms, API gateways, microservices, and real-time dashboards — with a strong grounding in testing, CI/CD, and cloud architecture on AWS and Google Cloud.

Today I bring that same production discipline to AI systems: LLM applications, retrieval, and document extraction that are evaluated, guardrailed, and cost-aware. I hold an M.S. in Computer Science from Georgia Tech, specialized in Interactive Intelligence and Machine Learning.