Kellie Ottoboni

Kellie Ottoboni is a researcher in the Department of Statistics at the University of California, Berkeley. She holds a B.A. and Ph.D. in Statistics from UC Berkeley, advised by Professor Philip Stark. Her research focuses on the intersection of statistics and social good, particularly in causal inference, experimental design, and election integrity. She has pioneered work on risk-limiting audits for elections and explored topics like noncompliance in experiments and gender bias in teaching evaluations.

• Education: B.A. in Applied Mathematics and Statistics (UC Berkeley), Ph.D. in Statistics (UC Berkeley)

Her work emphasizes practical applications of statistical methods to societal challenges, including election auditing systems, democratic process validation, and data governance in tech contexts. At Pinterest, she currently leads experimentation governance to ensure optimal design of app changes through rigorous statistical testing. She was a Fellow at the Berkeley Institute for Data Science (BIDS), where she contributed to open-source Python packages and explored distinctions between data science and traditional statistics.

Key research areas include hypothesis testing methodologies, voting machine analysis, and the limitations of student evaluations of teaching. Her contributions bridge theoretical statistical rigor with real-world policy applications in education, democracy, and technology governance.