I am a Research Scientist at Meta on the Statistics & Privacy team within Monetization Applied Research & Strategy (MARS). Before Meta, I was an Assistant Professor in the Social Science Research Institute, and the Department of Statistical Science at Duke University. I completed my PhD in Statistical Science at Duke, under the supervision of Dr. Jerry Reiter.
My research has been focused primarily on developing statistical methodology for handling missing and faulty data. More broadly, my research areas include Bayesian modeling, models for editing erroneous data, multiple imputation, missing data, mixture models, and hierarchical modeling. I am particularly interested in developing scalable methodology for handling missing data, measurement error, and conducting causal inference. I am excited about exploiting these ideas to improve business products at Meta, particularly in the Ads space.
Bayesian modeling, causal inference, hierarchical modeling, machine learning, missing data