Dan Kluger

I am a Michael Hammer Postdoctoral Fellow at the MIT Institute for Data, Systems, and Society, where I am fortunate to be hosted by Professors Sherrie Wang and Stephen Bates. I am broadly interested in developing statistical methods for drawing reliable conclusions from heterogeneous data sources that differ in quality, size, or resolution. My research is motivated by applications in agronomy, environmental science, and biomedical science, especially the types of heterogeneous data sets I have encountered when collaborating with scientists in these domains. My recent research focuses on developing methods for reliably leveraging widely available, yet error-prone estimates from machine learning models, and connects to the adaptive sampling, prediction-powered inference, measurement error, and missing data literatures.

Previously, I completed my PhD in Statistics at Stanford University, where I was grateful to be advised by Professors Art Owen and David Lobell and supported as a James and Nancy Kelso Interdisciplinary Graduate Fellow. While at Stanford, my research areas included multiple hypothesis testing, causal inference, and agronomy. Prior to graduate school, I received a B.S. in Mathematics and a B.A. in Statistics & Data Science at Yale University.