I'm a Senior Data Scientist at the University of Chicago's Biostatistics Laboratory, where I specialize in processing complex, unstructured data—from clinical text to time series analysis—with a focus on neurological research.
I love music just as much as I love data science (okay, maybe music slightly more). And after years of applying advanced statistical methods and machine learning to understand complex patterns in health data, I realized the same analytical rigor can dramatically improve how we discover music.
Traditional recommendation algorithms trap you in bubbles based on your listening history or generic user profiles. Stell-R takes a completely different approach—I've built it to map the actual semantic and sonic relationships between artists, creating organic influence pathways that let you discover music based purely on content, not past behavior.
The same data skills I use to analyze neural networks or estimate treatment effects are applied to something else I'm passionate about: helping people discover their next musical obsession.
My work focuses on unstructured data, end-to-end prediction pipelines, and causal inference—techniques that translate perfectly to understanding the complex web of musical influences and connections that traditional algorithms miss.
When I'm not building prediction pipelines for health research or refining Stell-R's algorithm, you'll find me running, boxing, record hunting, or (before I moved to Chicago) volunteering at The True Vine Record Shop in Baltimore (best record shop in the world!). But, mostly, always on the hunt for my next great musical discovery.