BIDS Data Science Fellow Kellie Ottoboni has been awarded the University of California Dissertation-Year Fellowship for 2018–2019. The award is open to doctoral students who have demonstrated strong potential for university teaching and research, leadership and academic service; with backgrounds, life experiences, and/or research interests that contribute to diversity.
Kellie is a graduate student in the Department of Statistics, and social impact has always been the center of her research interests. As she began her PhD, she made a commitment to also pursue leadership roles that would impact girls and women in her field, and advocate for women in STEM.
The theme of her dissertation is developing and applying hypothesis tests to real world data, and her first research paper explored gender bias in student evaluations of teaching. She has also studied the mechanics of the pseudorandom number generators and the null distributions of hypothesis tests, the basis of sound scientific results. Most recently, she has been developing statistical tests to carry out risk-limiting audits of elections, which helps give voters confidence that their ballot was counted, with lower cost and effort than a full recount.
In addition to developing new statistical methods and studying their theoretical properties, Kellie writes open source software implementing nonparametric methods in R and Python.
The award term will begin on August 30, 2018, for the 2018-2019 academic year.
Student evaluations of teaching (mostly) do not measure teaching effectiveness
January 7, 2016 | Anne Boring, Kellie Ottoboni, Philip B. Stark, | ScienceOpen Research
Simple Random Sampling: Not So Simple
February 7, 2017 | Kellie Ottoboni | BIDS Blog: Data Science Insights
Next Steps for the Colorado Risk-Limiting Audit (CORLA) Program
March 2, 2018 | Mark Lindeman, Neal McBurnett, Kellie Ottoboni, Philip B. Stark | arXiv.org (Preprint)