About Me

I've always been part mathemtican, part computer scientist and found my place in data science/engineering. I enjoy working in technical start-ups where I can do a variety of things and don't get stuck iterating on the same model endlessly or having to work in notebooks unless I want to.

After my first encounter with Vim almost 25 years ago, I have seen it's beauty and embraced NeoVim as my primary development tool (after years with Emacs and VS Code).

Math

My initial interest in computing was as a tool to do math, so I went to grad school in math with the plan of becoming a professor. I studied algebraic number theory and representation theory, building on a long interest in Fermat's Last Theorem and it's proof. Number theory is all about finding patterns, and figuring out how to explain those patterns.

When I had to teach statisitcs, I was first introduced to the field and realized it allowed me to combine my interest in patterns and data, computation, and work with real applications. I quickly migrated toward data science, even using machine learning to try to understand problems in number theory.

Academia to Tech

I spent 10 years after my Ph.D. teaching udergraduate mathematics. I enjoy teaching and had great opportunities to mentor impressive students and teach a wide range of courses, including several computer science classes. As my interests became more applied and I was doing more statistics and computing, I also became disappointed in being a professor for the next 30+ years. I decided to move to the tech industry and join a number of previous students (and several collegues) rather than prepare students for a world I had no experience in.

I became the first data scientist at a start-up, and I loved working closely with engineers and using my skills to solve problems for real people. Being in a start-up allowed me to gain experience in software engineering, data analystics, data science, and product management. This experience, combined with my background in math and computer science, made me into an adaptible generalist in data science and data engineering.