I am a postdoctoral fellow at Brigham and Women's Hospital and Harvard Medical School, developing predictive models in medicine using electronic health records (EHRs). Specifically, I am working on a Patient-Centered Outcomes Research Institute (PCORI) funded project to create advanced deep learning models for analyzing EHR data to better predict patients at risk for diabetes treatment failure. This work involves developing transformer neural networks for prediction on static patient features and temporal measurements, with the goal of identifying patients at risk of poor glycemic control and uncovering temporal patterns predictive of adverse outcomes.
Beyond predictive modeling, my research spans multiple areas at the intersection of machine learning and causal inference. I work on extending causal inference methods for multi-valued treatments, developing Bayesian frameworks for adversarial machine learning, and applying recurrent neural networks to panel data for causal impact estimation. My work also includes evaluating deep learning approaches for missing data imputation in large-scale survey data.
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
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction
Jason Poulos
Statistics and Public Policy, 2024
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
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
Adversarial Machine Learning: Bayesian Perspectives
David Rios Insua, Roi Naveiro, Víctor Gallego, Jason Poulos
Journal of the American Statistical Association, 2023
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
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
Character-Based Handwritten Text Transcription with Attention Networks
Rafael Valle, Jason Poulos
Neural Computing & Applications, 2021
Estimating Population Average Treatment Effects from Experiments with Noncompliance
Kellie Ottoboni, Jason Poulos
Journal of Causal Inference, 2020
Missing Data Imputation for Supervised Learning
Rafael Valle, Jason Poulos
Applied Artificial Intelligence, 2018
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
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction
Jason Poulos
Statistics and Public Policy, 2024
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
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
Adversarial Machine Learning: Bayesian Perspectives
David Rios Insua, Roi Naveiro, Víctor Gallego, Jason Poulos
Journal of the American Statistical Association, 2023
Gender Gaps in Frontier Entrepreneurship? Evidence from 1901 Oklahoma Land Lottery Winners
Jason Poulos
Journal of Historical Political Economy, 2023
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
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
Character-Based Handwritten Text Transcription with Attention Networks
Rafael Valle, Jason Poulos
Neural Computing & Applications, 2021
Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design
Jason Poulos
Journal of Historical Political Economy, 2021
Estimating Population Average Treatment Effects from Experiments with Noncompliance
Kellie Ottoboni, Jason Poulos
Journal of Causal Inference, 2020
Land Lotteries, Long-Term Wealth, and Political Selection
Jason Poulos
Public Choice, 2019
Missing Data Imputation for Supervised Learning
Rafael Valle, Jason Poulos
Applied Artificial Intelligence, 2018
Full CV: PDF.
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