Publications

^† indicates equal contributions
^‡ indicates students (co)-advised by me
^* indicates corresponding author(s)

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Preprints

Sakshi Arya^†, Hyebin Song^†*, Semi-Parametric Batched Global Multi-Armed Bandits with Covariates, ArXiv preprint, 2025+.
[Paper]

Kaitlyn Fales^‡*, Xurui Zhi^‡, Hyebin Song, Nicole Lazar, Replicability of Functional Brain Networks: A Study Through the Lens of the Default Mode Network, Submitted, 2025+.

Justin Petucci, Ian Sitarik, Yang Jiang, Viraj Rana, Hyebin Song^*, Edward O’Brien^*, Properties governing native state entanglements and relationships to protein function, Submitted, 2025+.

Ian Sitarik, Quyen Vu, Justin Petucci, Paulina Frutos, Hyebin Song, Edward O’Brien^*, A widespread protein misfolding mechanism is differentially rescued by chaperones based on gene essentiality, Submitted, 2025+.

Hyebin Song^†, Stephen Berg^†*, Weighted shape-constrained estimation for the autocovariance sequence from a reversible Markov chain, ArXiv preprint, 2025+.
[Paper]

Matthew Jensen^†, Corrine Smolen^†, Anastasia Tyryshkina^†, Lucilla Pizzo^†,…, Hyebin Song, et al.,…, and Santhosh Girirajan^*, Genetic modifiers and ascertainment drive variable expressivity of complex disorders, medRXiv preprint, 2024+.
[Paper]

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Publications

Statistical Methodology

Hyebin Song^†, Stephen Berg^†*, Multivariate moment least-squares estimators for reversible Markov chains, Journal of Graphical Statistics, 2024.
[Paper]

Stephen Berg^†, Hyebin Song^†*, Efficient shape-constrained inference for the autocovariance sequence from a reversible Markov chain, Annals of Statistics, 2023.
[Paper]

Ran Dai, Hyebin Song, Rina Foygel Barber^*, Garvesh Raskutti, Convergence guarantee for the sparse monotone single index model, Electronic Journal of Statistics, 2022.
[Paper]

Yi Ding^*, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hank Hoffmann, NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction, Proceedings of Machine Learning and Systems, 2022.
[Paper]

Hyebin Song^*, Garvesh Raskutti, Rebecca Willett, Prediction in the presence of response-dependent missing labels, IEEE Statistical Signal Processing Workshop, 2021.
[Paper]

Hyebin Song^*, Ran Dai, Garvesh Raskutti, Rina Foygel Barber, Convex and Non-convex Approaches for Statistical Inference with Class-Conditional Noisy Labels, Journal of Machine Learning Research, 2020.
[Paper]

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 (SIMODS), 2020.
[Paper]

Ran Dai, Hyebin Song, Rina Foygel Barber^*, Garvesh Raskutti, The bias of isotonic regression, Electronic Journal of Statistics, 2020.
[Paper]

Hyebin Song^*, Garvesh Raskutti, PUlasso: High-Dimensional Variable Selection With Presence-Only Data, Journal of the American Statistical Association, 2019.
[Paper] [Code]

Applications

Computational Biology and Applications in Protein Science

Yang Jiang^†, Yingzi Xia^†, Ian Sitarik, Piyoosh Sharma, Hyebin Song, Stephen Fried^*, Edward O’Brien^*, Protein misfolding involving entanglements provides a structural explanation for the origin of stretched-exponential refolding kinetics, Science Advances, 2025.
[Paper]

Viraj Rana^†, Ian Sitarik^†, Justin Petucci, Yang Jiang, Hyebin Song^*, Edward O’Brien^*, Non-covalent Lasso Entanglements in Folded Proteins: Prevalence, Functional Implications, and Evolutionary Significance, Journal of Molecular Biology, 2024.
[Paper]

Sameer D’Costa^†, Emily C. Hinds^†, Chase R. Freschlin, Hyebin Song^*, Philip A. Romero^*, Inferring protein fitness landscapes from laboratory evolution experiments, PLOS Computational Biology, 2023.
[Paper]

Hyebin Song, Bennett J. Bremer, Emily C. Hinds, Garvesh Raskutti, Philip A. Romero^*, Inferring protein sequence-function relationships with large-scale positive-unlabeled learning, Cell Systems, 2020.
[Paper]