Sang‐Yun Oh

University of California, Santa Barbara — United States

Mathematics · Physical Sciences

15h-index11.1kcitations56works0.52yr avg
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Research Topics

Statistical Methods and Inference(14), Cosmology and Gravitation Theories(9), Radio Astronomy Observations and Technology(6), Sparse and Compressive Sensing Techniques(5), Genetic Associations and Epidemiology(4)

Publications53 total

Learning massive-scale partial correlation networks in clinical multiomics studies with HP-ACCORD
The Annals of Applied Statistics·2025· 1 cited
Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD
arXiv (Cornell University)·2024Open Access
Induction of Osteogenesis After BMP-2 Injection in Rabbit Shoulders: A Novel Strategy for Managing Glenoid Bone Defect Associated with Anterior Shoulder Instability
World Journal of Surgery·2023· 1 citedOpen Access
Learning Gaussian graphical models with latent confounders
Journal of Multivariate Analysis·2023· 4 citedOpen Access
FROSTY: A High-Dimensional Scale-Free Bayesian Network Learning Method
Journal of Data Science·2023· 1 citedOpen Access
Family‐wise error rate control in Gaussian graphical model selection via distributionally robust optimization
Stat·2022· 1 citedOpen Access
Partial separability and functional graphical models for multivariate Gaussian processes
Biometrika·2021· 42 citedOpen Access
robsel: Robust Selection Algorithm
2021Open Access
Robust Selection Algorithm [R package robsel version 0.1.0]
2021
Endogenous Representation of Asset Returns
arXiv (Cornell University)·2020Open Access
fgm: Partial Separability and Functional Gaussian Graphical Models
2019· 1 cited
Partial Separability and Functional Gaussian Graphical Models [R package fgm version 1.0]
2019
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data
Machine Learning·2019· 19 citedOpen Access
Distributionally Robust Formulation and Model Selection for the Graphical Lasso
arXiv (Cornell University)·2019· 3 citedOpen Access
Distributionally Robust Formulation and Model Selection for the Graphical Lasso
arXiv (Cornell University)·2019· 3 citedOpen Access
Biomechanical influence of deficient posterolateral corner structures on knee joint kinematics: A computational study
Journal of Orthopaedic Research®·2018· 12 citedOpen Access
Communication-Avoiding Optimization Methods for Distributed\n Massive-Scale Sparse Inverse Covariance Estimation
arXiv (Cornell University)·2017· 4 citedOpen Access
Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning.
arXiv (Cornell University)·2017Open Access
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
arXiv (Cornell University)·2017· 8 citedOpen Access
Generalized pseudolikelihood methods for inverse covariance estimation
eScholarship (California Digital Library)·2017· 6 citedOpen Access
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Frequent Co-authors

A. Balbi(9), B. Rabii(9), C. D. Winant(9), Bala Rajaratnam(9), P. A. R. Ade(8), A. Boscaleri(8), P. de Bernardis(8), E. Pascale(8), G. F. Smoot(8), Pedro G. Ferreira(7), Jiun-Huei Proty Wu(7), J. J. Bock(6), V. V. Hristov(6), Kshitij Khare(6), Alexander Petersen(6), Shaul Hanany(5), J. Borrill(5), Andrew H. Jaffe(5), A. E. Lange(5), P. Mauskopf(5)