Sang‐Yun Oh

11.1k total citations
21 papers, 528 citations indexed

About

Sang‐Yun Oh is a scholar working on Molecular Biology, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Sang‐Yun Oh has authored 21 papers receiving a total of 528 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Artificial Intelligence and 5 papers in Statistics and Probability. Recurrent topics in Sang‐Yun Oh's work include Statistical Methods and Inference (5 papers), Radio Astronomy Observations and Technology (3 papers) and Cosmology and Gravitation Theories (3 papers). Sang‐Yun Oh is often cited by papers focused on Statistical Methods and Inference (5 papers), Radio Astronomy Observations and Technology (3 papers) and Cosmology and Gravitation Theories (3 papers). Sang‐Yun Oh collaborates with scholars based in United States, South Korea and Italy. Sang‐Yun Oh's co-authors include Bala Rajaratnam, Kshitij Khare, B. Rabii, C. D. Winant, Pedro G. Ferreira, Jiun-Huei Proty Wu, A. Balbi, Andrew H. Jaffe, J. J. Bock and G. F. Smoot and has published in prestigious journals such as The Astrophysical Journal, Biophysical Journal and Biometrika.

In The Last Decade

Sang‐Yun Oh

18 papers receiving 504 citations

Peers

Sang‐Yun Oh

NHEP

CVPR

Dalya Baron Israel

Tom Charnock France

Michelle Lochner South Africa

Shenghua Yu China

Nicholas M. Ball United States

E. Valiante United Kingdom

Zhiqi Huang China

Kai Polsterer Germany

Miles Cranmer United States

De Huang United States

Dalya Baron Israel

Sang‐Yun Oh

448 ×1.5

73 ×0.3

NHEP

18 ×0.3

66 ×1.2

27 ×0.8

CVPR

Citations per year, relative to Sang‐Yun Oh Sang‐Yun Oh (= 1×) peers Dalya Baron

Countries citing papers authored by Sang‐Yun Oh

Since Specialization

Citations

This map shows the geographic impact of Sang‐Yun Oh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sang‐Yun Oh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sang‐Yun Oh more than expected).

Fields of papers citing papers by Sang‐Yun Oh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sang‐Yun Oh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sang‐Yun Oh. The network helps show where Sang‐Yun Oh may publish in the future.

Co-authorship network of co-authors of Sang‐Yun Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Sang‐Yun Oh. A scholar is included among the top collaborators of Sang‐Yun Oh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sang‐Yun Oh. Sang‐Yun Oh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Oh, Sang‐Yun, et al.. (2025). Learning massive-scale partial correlation networks in clinical multiomics studies with HP-ACCORD. The Annals of Applied Statistics. 19(4).

Kim, Sae Hoon, et al.. (2023). 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. 1–8. 1 indexed citations

Franks, Alexander, et al.. (2023). Learning Gaussian graphical models with latent confounders. Journal of Multivariate Analysis. 198. 105213–105213. 1 indexed citations

Tran, Chau, et al.. (2022). Family‐wise error rate control in Gaussian graphical model selection via distributionally robust optimization. Stat. 11(1).

Oh, Sang‐Yun, et al.. (2021). Partial separability and functional graphical models for multivariate Gaussian processes. Biometrika. 109(3). 665–681. 21 indexed citations

Khare, Kshitij, Sang‐Yun Oh, Syed M Rahman, & Bala Rajaratnam. (2019). A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data. Machine Learning. 108(12). 2061–2086. 12 indexed citations

Kang, Kyoung‐Tak, Yong‐Gon Koh, Juhyun Son, et al.. (2018). Biomechanical influence of deficient posterolateral corner structures on knee joint kinematics: A computational study. Journal of Orthopaedic Research®. 36(8). 2202–2209. 7 indexed citations

Ali, Alnur, Kshitij Khare, Sang‐Yun Oh, & Bala Rajaratnam. (2017). Generalized pseudolikelihood methods for inverse covariance estimation. eScholarship (California Digital Library). 54. 280–288. 3 indexed citations

Ali, Alnur, Ariful Azad, Aydın Buluç, et al.. (2017). Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation. arXiv (Cornell University). 84. 1376–1386. 4 indexed citations

10.

Racah, Evan, Peter Sadowski, Wahid Bhimji, et al.. (2016). Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. eScholarship (California Digital Library). 892–897. 7 indexed citations

11.

Azad, Ariful, Aydın Buluç, Dmitriy Morozov, et al.. (2016). Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication. eScholarship (California Digital Library). 842–853. 27 indexed citations

12.

Racah, Evan, Peter Sadowski, Wahid Bhimji, et al.. (2016). Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. arXiv (Cornell University). 9 indexed citations

13.

Khare, Kshitij, Sang‐Yun Oh, & Bala Rajaratnam. (2014). A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees. Journal of the Royal Statistical Society Series B (Statistical Methodology). 77(4). 803–825. 61 indexed citations

14.

Choi, Wonseok, et al.. (2012). Vision-based fusion of robust lane tracking and forward vehicle detection in a real driving environment. International Journal of Automotive Technology. 13(4). 653–669. 30 indexed citations

15.

Choi, Hyun‐Chul & Sang‐Yun Oh. (2010). Modified second-order minimisation for AAM fitting. Electronics Letters. 46(13). 913–914. 2 indexed citations

16.

Lee, Deukhwan, Sang‐Yun Oh, & Niki C Whitley. (2009). Study on Genetic Evaluation for Linear Type Traits in Holstein Cows. Asian-Australasian Journal of Animal Sciences. 23(1). 1–6. 2 indexed citations

17.

Lee, Younho, Sang‐Yun Oh, Young-Joo Suh, Seonghyung Jang, & Woontack Woo. (2007). Enhanced Framework for a Personalized User Interface Based on a Unified Context-Aware Application Model for Virtual Environments. IEICE Transactions on Information and Systems. E90-D(6). 994–997. 4 indexed citations

18.

Balbi, A., P. A. R. Ade, J. J. Bock, et al.. (2002). CONSTRAINTS ON COSMOLOGICAL PARAMETERS FROM MAXIMA-1. ORCA Online Research @Cardiff (Cardiff University). 2195–2196. 2 indexed citations

19.

Rosen, J. B., A. T. Phillips, Sang‐Yun Oh, & Ken A. Dill. (2000). A Method for Parameter Optimization in Computational Biology. Biophysical Journal. 79(6). 2818–2824. 7 indexed citations

20.

Balbi, A., P. A. R. Ade, J. J. Bock, et al.. (2000). Constraints on Cosmological Parameters from MAXIMA-1. The Astrophysical Journal. 545(1). L1–L4. 294 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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