Hyebin Song is an Assistant Professor of Statistics at Penn State.
Song received her PhD in Statistics from the University of Wisconsin-Madison in 2020. She received her BA in Applied Statistics from Yonsei University in 2012.
Her research interests focus on developing methods to overcome statistical and computational challenges in modern data sets, which are often complex, large-scale, and high-dimensional. She is especially interested in developing statistical techniques to help improve our understanding of biological structures from data.
Song has previously worked in the central bank of Korea as a Statistician. She joined Penn State as an Assistant Professor in 2020.
Honors and Awards
ASA SLDS Student Paper Competition Winner in 2018.
- Hyebin Song, Bennett J. Bremer, Emily C. Hinds, Garvesh Raskutti, Philip A. Romero. Inferring protein sequence-function relationships with large-scale positive-unlabeled learning. In Press, Cell Systems, 2020
- Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber. “Convex and Non-convex Approaches for Statistical Inference with Noisy Labels”, Journal of Machine Learning Research, 2020
- Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman, “Graph-based regularization for regression problems with alignment and highly-correlated designs”, SIAM Journal on Mathematics of Data Science, 2020.
- Ran Dai, Hyebin Song, Rina Foygel Barber, Garvesh Raskutti, “The bias of isotonic regression”, Electronic Journal of Statistics, 2020.
- Hyebin Song, Garvesh Raskutti. “PUlasso: High-dimensional variable selection with presence-only data.” Journal of the American Statistical Association, 2018.