Hello, this is Chris Riederer. Professionally, I'm a data scientist at Propel, where I build products for low income Americans. Before that I was a machine learning engineer at Square's Cash App. Previously I received a Ph.D. in Computer Science from Columbia University in New York City. You can find my Google Scholar here and my academic website here. This site contains information on my professional background as well as some goofy projects I've completed over the years.
At Cash App, I built the machine learning infrastructure and algorithms for our Customer Success team, allowing us to intelligently prioritize, efficiently route, and automatically resolve support cases. Another machine learning project I launched detected when customers were sending money to the wrong person to help warn them of their mistake. I typically program in Python, R, and Java, but I've also used C, C#, Scala, Gurobi, Clojure, and JavaScript.
My Ph.D. thesis applied machine learning, optimization, and mathematical modeling to the challenge of privacy and algorithmic bias, specifically focused on location data. My work on de-anonymizing accounts using location data was featured in the popular press, such as here.
Outside of my Ph.D. research, I launched the developer tutorial for Google Cardboard (a virtual reality device), as well as used machine learning to improve the function of it's magnetometer based input. During a stint at Microsoft Research, I launched a feature to help Bing users put the scale of countries into perspective. During another stint at MSR, I helped create a tool that rewrites app binaries to stop prefetching data, procrastinating network calls until they are actually needed, thereby saving the user's data (and hopefully their dollars). I've also worked as a consulting data scientist at a start up focused on connecting users to governmental benefits and volunteered teaching coding to teenagers in New York schools and Rikers Island.