Hyebin Song

Hyebin Song is an Assistant Professor of Statistics at the Pennsylvania State University since 2020. She holds a PhD in Statistics from the University of Wisconsin-Madison (2020) and a BA in Applied Statistics from Yonsei University (2012). Previously, she worked as a Statistician at the Bank of Korea. Her research focuses on developing statistical methodologies for high-dimensional and complex datasets, with applications in neuroscience, systems biology, and computational protein modeling. Key areas include semi-parametric inference, statistical learning, and computational biology. She has developed influential software tools like PUlasso for high-dimensional variable selection and contributed to advancements in Markov chain autocovariance estimation. Her work has been recognized with the ASA SLDS Student Paper Competition Award (2018).

Education: PhD in Statistics (UW-Madison, 2020), BA in Applied Statistics (Yonsei University, 2012). Current affiliations include the Department of Statistics at Penn State and collaborations with computational biology labs. She actively advises PhD students in statistical methodology and applications.

Research interests span high-dimensional statistics, shape-constrained inference, and statistical computing, with applications to protein structure analysis and functional genomics. Notable software includes the 'momentLS' package for Markov chain estimation and 'pudms' for deep mutational scanning analysis.