For my undergraduate degree, I studied Mathematics at the State University of New York at Geneseo. I then worked from 2014-2018 as a software developer at Silent Spring Institute in Newton, Massachusetts, where I worked on methods for making complex scientific information accessible to lay audiences.
From 2018-2022 I pursued a PhD in Biostatistics in the Department of Biostatistics & Epidemiology at the University of Massachusetts Amherst, with Leontine Alkema as my advisor. For the 2021-2022 academic year I studied with Antoine Chambaz at the MAP5 laboratory, Université de Paris, on a Chateaubriand Fellowship.
My thesis was titled “Bayesian Hierarchical Temporal Modeling and Targeted Learning with Application to Reproductive Health”. The slides I used for the defense give a nice overview of the thesis, and are available here.
In 2023 I was a post-doctoral researcher with the CNRS (in the CEREMADE laboratory at Université Paris Dauphine - PSL). I worked with Emmanuel Bacry, Antoine Chambaz, and Julie Josse on methods for ensemble time-series prediction.
I am currently a post-doctoral researcher in the Division of Biostatistics, New York University Grossman School of Medicine. I am working with Iván Díaz on methods in causal inference.
My current research interests include causal inference, semi-parametric statistics, and probabilistic forecasting. I’m interested in applications in global health, climate change, and political economy.
