From Tangled Lassos to Boosted Trees: Iterative Research in Practice By Emily Dodwell Abstract: The development of a machine learning-based media targeting strategy for television advertising campaigns introduces computational challenges inherent in the scale of training data. Features derived from customer viewership records necessitate a robust and scalable solution. Emily will discuss potential solutions her team considered to tackle this business problem in R, as well as the theoretical intuition for the final two machine learning algorithms they chose to compare for implementation. Bio: Emily Dodwell is a Senior Inventive Scientist in the Data Science and AI Research organization at AT&T Labs, where she currently focuses on predictive modeling for advertising applications, the creation of interactive tools for data analysis and visualization, and research concerning ethics and fairness in machine learning. She is a member of R Forwards, the R Foundation task force on women and other underrepresented groups. Prior to joining AT&T Labs in 2015, Emily taught high school math for three years at Choate Rosemary Hall. She received her M.A. in statistics from Yale University and B.A. in mathematics from Smith College. Twitter: @emdodwell Presented at the 2019 New York R Conference (May 10th, 2019)