Real name:
Kellie OttoboniKellie Ottoboni is a former BIDS Data Science Fellow and a graduate of UC Berkeley's Department of Statistics. Her research at BIDS focused on using robust nonparametric statistics and machine learning to make causal inferences from data in the health and social sciences. The goal was 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 wrote open source software implementing nonparametric methods in R and Python.
Projects
Berkeley Carpentries Club
Status: Archived
Events
March 14, 2016 / 3:30pm to 3:40pm
Blog Posts
Piloting Risk-Limiting Audits in Michigan
December 20, 2018
Restoring voter confidence with data science
July 16, 2018
Simple Random Sampling: Not So Simple
February 3, 2017
Bringing Data Science Back to Statistics
February 16, 2016
Featured in the News
Publications
February 1, 2019
December 15, 2018
November 14, 2018
September 6, 2018