Ying Zhang
About Ying Zhang
Ying Zhang is a Data Scientist at Two Sigma in New York, specializing in spatio-temporal data analysis, particularly in air pollution modeling in China. With a PhD in Statistics from Penn State University, Zhang's research focuses on Bayesian decision theory and its applications in environmental data.
Work at Two Sigma
Ying Zhang has been employed as a Data Scientist at Two Sigma since 2021. In this role, she focuses on spatio-temporal data analysis, specifically modeling air pollution in China. Her work aims to provide insights that contribute to pollution control efforts. Prior to her current position, she worked as a Quantitative Research Intern at Two Sigma for two months in 2020, where she gained experience in quantitative research methodologies.
Education and Expertise
Ying Zhang is currently pursuing a Doctor of Philosophy (PhD) in Statistics at Penn State University, a program she joined in 2017. Her research emphasizes quantifying uncertainties in Bayesian decision theory, with applications in model selection, interpretation, and rare event detection. She also has a background in Statistics from the University of California, Davis, where she studied from 2015 to 2016, and holds a Bachelor of Science (B.Sc.) in Statistics from Renmin University of China, completed in 2017.
Background
Before her role at Two Sigma, Ying Zhang worked as a Graduate Assistant at Penn State University from 2017 to 2021. During this time, she supported various academic and research initiatives while furthering her studies in Statistics. Her academic journey has provided her with a solid foundation in statistical methodologies and their applications in environmental data.
Research Interests
Ying Zhang's research interests lie in Bayesian inference, particularly its practical applications in environmental data. She focuses on modeling air pollution and aims to provide actionable insights for pollution control. Her work in quantifying uncertainties in Bayesian decision theory contributes to the fields of model selection and rare event detection.