Clinton Brownley, Ph.D.

Lead Data Scientist | Tala

Clinton Brownley, Ph.D., is currently a lead data scientist at Tala with a focus on causal inference, machine learning, and experimentation. Prior to this role, he was a data scientist at Meta (formerly Facebook), where he was responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and datacenter operations decisions at Facebook. As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley's MIDS program and a graduate course in regression analysis for NYU's A3SR program. He also leads an annual machine learning in python workshop at the ML Week and ODSC West conferences. Clinton is also the author of two books, "Foundations for Analytics with Python" and Multi-objective Decision Analysis". Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.


Visualization in Bayesian Workflow Using Python or R

Visualization can be a powerful tool to help you build better statistical models. In this tutorial, you will learn how to create and interpret visualizations that are useful in each step of a Bayesian regression workflow. A Bayesian workflow includes the three steps of (1) model building, (2) model interpretation, and (3) model checking/improvement, along with model comparison. Visualization is helpful in each of these steps – generating graphical representations of the model and plotting prior distributions aid model building, visualizing MCMC diagnostics and plotting posterior distributions aid interpretation, and plotting posterior predictive, counterfactual, and model comparisons aid model checking/improvement.


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