Hi — I’m the engineer behind this blog.

My background

I started as an electrical engineer working on data analysis and signal processing—building intuition around signals, noise, and randomness from day one. I worked across wireless industries, from communication satellites to high-end commercial WiFi routers, constantly building hardware and software automation to characterize and test end-to-end systems, using software.

That's when I fell in love with software. I realized it was the glue that bound complex systems together. Software led me to data science, where I spent four years productionizing forecasting models before moving into marketing to build automated messaging platform.

In marketing, I learned about A/B testing frameworks and dove deep into experimentation science. That's where it all clicked: everything I'd been doing—from signal processing to forecasting to experiments—was really just about separating signal (ie information) from noise (ie uncertainty).

What I'm interested in

• Turning complex statistical concepts into practical engineering tools
• Agentic orchestration and eval design
• Experimentation design and causal inference
• Building smart large-scale distributed systems
• Probabilistic programming

Why I started this blog

I strongly believe that engineers should own the end to end experience of their products. That involves measuring end customer experience and working backward to design and tune every system that can have an impact on the end user experience. You cannot improve or fix that which you cannot measure. Proper measurements involves analysis and intuition building. With the right statistical intuition, you can debug smarter, design better experiments, and make decisions with much more confidence.

My goal here is to talk about statistics the way engineers actually think — through systems, monitoring, experiments, and real-world examples rather than textbook jargon.