Jason Poulos is in the SAMSI Program on Causal Inference. He received his Ph.D. in Political Science with a Designated Emphasis in Computational Science and Engineering from the University of California, Berkeley. His research focuses on developing methods that leverage machine learning for causal inference, with social science applications. He is currently working on counterfactual estimators for panel data and methods to improve the transportability of randomized control trial results to a population. He enjoys trail running, weightlifting, and collecting vinyl.