I am a Postdoctoral Fellow in the Division of Biostatistics in the NYU Grossman School of Medicine, where I am fortunate to work with Iván Díaz and Wenbo Wu. We are developing theory and methods at the intersection of machine learning and causal inference, and applying them to problems in healthcare provider profiling and personalized medicine.

Before my postdoc, I did my PhD in Statistics at Carnegie Mellon University, where I was advised by Zach Branson and Edward Kennedy. Additionally, I had the pleasure of working with Siva Balakrishnan and Larry Wasserman.

My primary research interests are at the intersection of statistics, machine learning, and causal inference. Some specific topics I have worked on include

During my PhD, I also gained invaluable teaching experience. Twice, I instructed CMU’s Sophomore-level Introduction to Statistical Inference course. Additionally, I had the opportunity to serve as a teaching assistant for several courses related to causal inference and other areas of statistics.

I completed my undergraduate education at Swarthmore College, where I earned a Bachelor’s degree in Mathematics and Economics in 2016. Before starting my PhD, I worked for three years as a Research Analyst and later as a Senior Research Analyst at the Brattle Group. There, I helped analyze data and build statistical models for legal, regulatory, and policy issues. In particular, I learned a huge amount from Charlie Gibbons, who inspired me to pursue a career in econometrics/statistics/ML.


Connect


Papers

Google Scholar and CV (current as of June 2024)


Software

Contributor to npcausal package


News

(Jul ‘24) I started as a Postdoctoral fellow at NYU working with Iván Díaz and Wenbo Wu!

(May ‘24) I presented our ongoing work on calibrated sensitivity models at ACIC 2024 (slides). A working paper is up on arxiv

(Mar ‘24) We have a new paper on arxiv on double cross-fit doubly robust estimators!


Teaching

As Course Instructor

As Teaching Assistant


Service