Kellie Ottoboni

Graduate Student, Department of Statistics
BIDS Data Science Fellow, StateStreet

Real name: 
Kellie Ottoboni

Kellie Ottoboni is a graduate student in the Department of Statistics. Her research focuses on using robust nonparametric statistics and machine learning to make causal inferences from data in the health and social sciences. The goal is to make reliable inferences while making minimal assumptions about the models generating the data. In addition to developing new statistical methods and studying their theoretical properties, Kellie writes open source software implementing nonparametric methods in R and Python.