Speaker: Chris Wiggins
Title: Data Science at The New York Times
Abstract: The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks requires not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. I'll first outline how unsupervised, supervised, and reinforcement learning methods are increasingly used in human applications for; description, prediction, and prescription, respectively. I'll then focus on the 'prescriptive' cases, showing how methods from the reinforcement learning and causal inference literatures can be of direct impact in; engineering, business, and decision-making more generally.