Roni Kobrosly Ph.D.'s Website

Obesity, as a public health problem, has an enormous amount of "causes": the types of farmed foods we tend to subsidize on a national level, our policies around public transit, the walkability of neighorhoods, the presence of food deserts, our social networks and their attitudes toward obesity, the media, etc etc. All of these complex, interconnected things make it really challenging to perform a causal analysis of potential solutions. I recently came across a great paper that takes a stab at addressing this problem through simulations, and I think the lessons from this are very much applicable to some problems we face in the data industry.

II’m in my mid-thirties and as many of my friends are starting to make their own families, I'm having to learn lots of baby names. I’ve heard lots of people say that “older” names are becoming popular and in hearing these baby names I feel like there is something to this. What kind of name trends exist out there?

Sometimes when you're tuning a parameter in a machine learning, you end up needing to look at something like a scree plot to determine the best parameter value. It feels annoying and subjective. Here's a simple way to automate this away.

When we create maps to view the spatial variation of socioeconomic status, we are typically only viewing the variation of one factor at a time (e.g. just income or just unemployment rate). I thought it would be useful to create and visualize a summary score of overall "socioeconomic disadvantage" from many socioeconomic indicators.