From the Periphery
The most interesting problems don't live in the center of any single discipline. They live at the edges—where business logic meets statistical models, where technical feasibility meets organizational reality, where academic rigor meets messy real-world data.
This blog is written from that periphery.
What I Do
I help make sense of data by finding ways to measure what actually matters. Not vanity metrics, not whatever's easiest to count—but interpretable quantities that connect to real decisions. The gap between what we measure and what we care about is where most data projects fail. Closing that gap is what I work on.
What You'll Find Here
Proxy Metrics and Their Limits — We rarely measure what we actually care about. Understanding when a proxy works and when it breaks is half the job.
Making Models Interpretable — A prediction without explanation is often useless. I'll share approaches that connect model outputs to human reasoning.
Practical Automation — Not everything needs machine learning. Sometimes the right workflow design solves the problem better than any model.
A Book Worth Reading
If you work with data, read Laura Sebastian-Coleman's Measuring Data Quality for Ongoing Improvement. It's the clearest treatment I've found on how to think about data quality as something you can actually quantify and track.
Why Here?
You might find my shorter thoughts on LinkedIn or Medium. But those platforms optimize for their metrics, not yours. Here, I own the content. No algorithms deciding what you see. No paywall games. Just ideas, available to anyone who finds them useful.
If something resonates, reach out. If something's wrong, tell me. That's the point.
More posts coming. Subscribe to nothing—just bookmark the page if you want to come back.