Hello, I’m Scott. I lecture in the applied analytics graduate program at Columbia University, my alma mater. I work with core developers of Stan, a probabilistic programming language, building Bayesian, generative models of complex processes involving both human behavior and physical phenomena.

My applied work focuses on modeling data for good and on professional sports. Recent efforts are on the spatial and temporal impact of sea-level rise on the perceived value of coastal land, and on major league baseball player and team performance. My work in modeling and communication arise from a doctor of jurisprudence, master of science in sports management focused on data science analytics, and bachelor of science in chemical engineering focused on numerical methods and statistical process control.

My analyses have extended to other domains as well. Previously, I analyzed data and communicated statistical insights, persuading various stakeholders of my technology clients (including Vevo, Freewheel, Johnson & Johnson, Dow Agrosciences, Qualcomm, Fitbod, and Amazon) on the impact of my analyses for decisions related to patent litigation and licensing.

To assist visual communication, I have coded multiple R packages, including one for mapping data values to visual encodings perceptually-uniform separately across hue, saturation, and luminance. The most persuasive communications are transparent and account for uncertainty, which are two areas of interest in my research and work in quantitative communication through visualization and storytelling. Along with research I have collaborated and published variously. These include analyses, editing, and research for The Real Madrid Way (BenBella Books 2016), of which Billy Bean (Executive Vice President of Baseball Operations at the Oakland A’s) has said “will be one of the most influential books on sports ever written.” I have a forthcoming monograph and literature review on quantitative communication amid uncertainty.

Along with honors recognition for research and writing, my visualizations have been showcased and longlisted in the Kantar Information is Beautiful Awards, and my analyses have won analytics competitions, including the Society for American Baseball Research’s analytics competition, graduate division for my work involving human decision-making based on perception of spatial and temporal event information. I am fluent in R, code in Stan for Bayesian modeling, ggplot2, D3.js, and Processing for visualization. Other tools include Git, Rmd, SQL, C++, macOS, and Adobe CC.