I build things with math and computers!
I like to use the language and logic of probability to quantify uncertainty and frame problems. You could call me a statistician and, more specifically, a Bayesian [statistician].
My interests (personal and professional) revolve around the means and methods of mathematical modeling and its automation. I’m a big proponent of relational programming and symbolic computation, and I promote their use in research and industry whenever I can.
Professionally, I work the whole gamut of design (e.g. formal mathematics, publishable papers, technical reports) and development (e.g. numerical methods research, production-level software projects, large-scale deployments, task automations, etc.) My career has covered the areas of finance, transportation, energy, start-ups, gov-tech, and academia—often during the research and planning phases where flexibility and foresight are truly needed.
Here are some situations in which I can be of service:
- When popular statistical modeling or machine learning libraries are inadequate (or unavailable on a given platform).
- When combining or orchestrating sophisticated models in non-trivial ways.
- When dealing with high-dimensional, real-time, and/or discrete processes.
- When model selection is needed for non-standard models.
- When a complex situation demands that data be handled in [statistically] principled ways.
Here’s a live copy of my CV.
Here are some things I’ve been involved in:
- Google Summer of Code (as a mentor for PyMC under NumFOCUS)
- Data Science for Social Good (as an inaugural mentor)
- MTA Bus Time