Jason Poulos

poulos [AT] berkeley.edu

Bio

My research lies at the intersection of machine learning, causal inference, and health data science. I develop and apply machine (deep) learning models to address challenges in complex, high-dimensional longitudinal health data.

I earned a Ph.D. in Political Science with a Designated Emphasis in Computational Science and Engineering from UC Berkeley. As a postdoctoral fellow at Brigham and Women's Hospital and Harvard Medical School, I developed transformer-based models for predicting diabetes treatment outcomes using electronic health records.

My broader research agenda includes extending causal inference methods for multi-valued treatments, developing Bayesian frameworks for adversarial machine learning, applying recurrent neural networks to panel data for causal impact estimation, and evaluating deep learning approaches for missing data imputation in survey data.

I am actively exploring research scientist roles in tech and health sciences, aiming to leverage my expertise in machine learning and causal inference to solve complex problems and advance knowledge in these domains.

Publications

Most recent publications on Google Scholar.

Revisiting Diabetes Risk of Olanzapine versus Aripiprazole for Serious Mental Illness Care

Denis Agniel, Sharon-Lise Normand, John Newcomer, Katya Zelevinsky, Jason Poulos, Jeannette Tsuei, Marcela Horvitz-Lennon

BJPsych Open, 2024

Paper

State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction

Jason Poulos

Statistics and Public Policy, 2024

Paper Preprint Code

Targeted Learning in Observational Studies with Multi-Level Treatments: An Evaluation of Antipsychotic Drug Treatment Safety

Jason Poulos, Marcela Horvitz-Lennon, Katya Zelevinsky, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Tudor Cristea-Platon, Sharon-Lise Normand

Statistics in Medicine, 2024

Paper Preprint Code

Antipsychotics and the Risk of Diabetes and Death among Adults with Serious Mental Illnesses

Jason Poulos, Sharon-Lise Normand, Katya Zelevinsky, John Newcomer, Denis Agniel, Haley Abing, Marcela Horvitz-Lennon

Psychological Medicine, 2023

Paper Code

Adversarial Machine Learning: Bayesian Perspectives

David Rios Insua, Roi Naveiro, Víctor Gallego, Jason Poulos

Journal of the American Statistical Association, 2023

Paper Preprint Code

Are Deep Learning Models Superior for Missing Data Imputation in Surveys? Evidence from an Empirical Comparison

Zhenhua Wang, Olanrewaju Akande, Jason Poulos, Fan Li

Survey Methodology, 2022

Paper Preprint Code

RNN-Based Counterfactual Prediction, with an Application to Homestead Policy and Public Schooling

Jason Poulos, Shuxi Zeng

Journal of the Royal Statistical Society (C), 2021

Paper Preprint Code

Character-Based Handwritten Text Transcription with Attention Networks

Jason Poulos, Rafael Valle

Neural Computing & Applications, 2021

Paper Preprint Code

Estimating Population Average Treatment Effects from Experiments with Noncompliance

Kellie Ottoboni, Jason Poulos

Journal of Causal Inference, 2020

Paper Preprint Code

Missing Data Imputation for Supervised Learning

Jason Poulos, Rafael Valle

Applied Artificial Intelligence, 2018

Paper Preprint Supplementary Material Code

Revisiting Diabetes Risk of Olanzapine versus Aripiprazole for Serious Mental Illness Care

Denis Agniel, Sharon-Lise Normand, John Newcomer, Katya Zelevinsky, Jason Poulos, Jeannette Tsuei, Marcela Horvitz-Lennon

BJPsych Open, 2024

Paper

State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction

Jason Poulos

Statistics and Public Policy, 2024

Paper Preprint Code

Targeted Learning in Observational Studies with Multi-Level Treatments: An Evaluation of Antipsychotic Drug Treatment Safety

Jason Poulos, Marcela Horvitz-Lennon, Katya Zelevinsky, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Tudor Cristea-Platon, Sharon-Lise Normand

Statistics in Medicine, 2024

Paper Preprint Code

Antipsychotics and the Risk of Diabetes and Death among Adults with Serious Mental Illnesses

Jason Poulos, Sharon-Lise Normand, Katya Zelevinsky, John Newcomer, Denis Agniel, Haley Abing, Marcela Horvitz-Lennon

Psychological Medicine, 2023

Paper Code

Adversarial Machine Learning: Bayesian Perspectives

David Rios Insua, Roi Naveiro, Víctor Gallego, Jason Poulos

Journal of the American Statistical Association, 2023

Paper Preprint Code

Gender Gaps in Frontier Entrepreneurship? Evidence from 1901 Oklahoma Land Lottery Winners

Jason Poulos

Journal of Historical Political Economy, 2023

Paper Preprint Code

Are Deep Learning Models Superior for Missing Data Imputation in Surveys? Evidence from an Empirical Comparison

Zhenhua Wang, Olanrewaju Akande, Jason Poulos, Fan Li

Survey Methodology, 2022

Paper Preprint Code

RNN-Based Counterfactual Prediction, with an Application to Homestead Policy and Public Schooling

Jason Poulos, Shuxi Zeng

Journal of the Royal Statistical Society (C), 2021

Paper Preprint Code

Character-Based Handwritten Text Transcription with Attention Networks

Jason Poulos, Rafael Valle

Neural Computing & Applications, 2021

Paper Preprint Code

Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design

Jason Poulos

Journal of Historical Political Economy, 2021

Paper Preprint Code

Estimating Population Average Treatment Effects from Experiments with Noncompliance

Kellie Ottoboni, Jason Poulos

Journal of Causal Inference, 2020

Paper Preprint Code

Land Lotteries, Long-Term Wealth, and Political Selection

Jason Poulos

Public Choice, 2019

Paper Code

Missing Data Imputation for Supervised Learning

Jason Poulos, Rafael Valle

Applied Artificial Intelligence, 2018

Paper Preprint Supplementary Material Code

Vitæ

Full CV: PDF.

Jason Poulos
poulos [AT] berkeley.edu