Bryon Aragam

794 total citations
20 papers, 292 citations indexed

About

Bryon Aragam is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Bryon Aragam has authored 20 papers receiving a total of 292 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Statistics and Probability and 4 papers in Molecular Biology. Recurrent topics in Bryon Aragam's work include Bayesian Modeling and Causal Inference (5 papers), Statistical Methods and Inference (5 papers) and Genetic Associations and Epidemiology (4 papers). Bryon Aragam is often cited by papers focused on Bayesian Modeling and Causal Inference (5 papers), Statistical Methods and Inference (5 papers) and Genetic Associations and Epidemiology (4 papers). Bryon Aragam collaborates with scholars based in United States and Netherlands. Bryon Aragam's co-authors include Eric P. Xing, Haohan Wang, Benjamin J. Lengerich, Pradeep Ravikumar, Xun Zheng, Qing Zhou, Jiaying Gu, Bingjing Zhang, Pradeep Ravikumar and Francesco Locatello and has published in prestigious journals such as Journal of the American Statistical Association, Bioinformatics and The Annals of Statistics.

In The Last Decade

Bryon Aragam

15 papers receiving 286 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Bryon Aragam United States	7	104	100	41	36	35	20	292
Grace S. Shieh Taiwan	14	103 1.0×	251 2.5×	20 0.5×	48 1.3×	34 1.0×	40	495
Weichen Wang United States	9	49 0.5×	153 1.5×	56 1.4×	60 1.7×	39 1.1×	22	466
Xiwei Tang United States	9	56 0.5×	198 2.0×	45 1.1×	68 1.9×	18 0.5×	26	414
Wenyu Jiang Canada	8	60 0.6×	82 0.8×	14 0.3×	49 1.4×	15 0.4×	30	338
Theodore Papamarkou United Kingdom	9	54 0.5×	95 0.9×	34 0.8×	22 0.6×	7 0.2×	25	255
Miguel Henriques Abreu Portugal	10	136 1.3×	96 1.0×	37 0.9×	44 1.2×	49 1.4×	22	370
Changyi Park South Korea	9	75 0.7×	77 0.8×	13 0.3×	19 0.5×	23 0.7×	27	277
Thibault Helleputte Belgium	8	181 1.7×	328 3.3×	43 1.0×	46 1.3×	20 0.6×	16	642
Bernard Omolo United States	11	66 0.6×	127 1.3×	55 1.3×	44 1.2×	6 0.2×	33	339
Wentian Guo China	10	105 1.0×	86 0.9×	68 1.7×	65 1.8×	13 0.4×	15	516

Countries citing papers authored by Bryon Aragam

Since Specialization

Citations

This map shows the geographic impact of Bryon Aragam'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 Bryon Aragam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bryon Aragam more than expected).

Fields of papers citing papers by Bryon Aragam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bryon Aragam. 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 Bryon Aragam. The network helps show where Bryon Aragam may publish in the future.

Co-authorship network of co-authors of Bryon Aragam

This figure shows the co-authorship network connecting the top 25 collaborators of Bryon Aragam. A scholar is included among the top collaborators of Bryon Aragam 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 Bryon Aragam. Bryon Aragam 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

Aragam, Bryon, et al.. (2026). Towards Interpretable Deep Generative Models via Causal Representation Learning. Journal of the American Statistical Association. 1–32.

Aragam, Bryon, et al.. (2024). Identifying General Mechanism Shifts in Linear Causal Representations. 42405–42429.

Aragam, Bryon, et al.. (2023). Uniform consistency in nonparametric mixture models. The Annals of Statistics. 51(1). 1 indexed citations

Aragam, Bryon, et al.. (2023). iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. 44671–44706.

Wang, Haohan, Bryon Aragam, & Eric P. Xing. (2022). Trade-offs of Linear Mixed Models in Genome-Wide Association Studies. Journal of Computational Biology. 29(3). 233–242. 5 indexed citations

Aragam, Bryon, et al.. (2022). DAGMA: Learning DAGs Via M-Matrices and a Log-Determinant Acyclicity Characterization. 8226–8239.

Aragam, Bryon, et al.. (2019). Globally optimal score-based learning of directed acyclic graphs in high-dimensions. Neural Information Processing Systems. 32. 4450–4462. 2 indexed citations

Inouye, David I., et al.. (2019). Diagnostic Curves for Black Box Models.. arXiv (Cornell University). 1 indexed citations

Aragam, Bryon, Jiaying Gu, & Qing Zhou. (2019). Learning Large-Scale Bayesian Networks with the sparsebn Package. Journal of Statistical Software. 91(11). 26 indexed citations

10.

Lengerich, Benjamin J., Bryon Aragam, & Eric P. Xing. (2019). Learning Sample-Specific Models with Low-Rank Personalized Regression. arXiv (Cornell University). 32. 3570–3580. 4 indexed citations

11.

Lengerich, Benjamin J., Bryon Aragam, & Eric P. Xing. (2018). Personalized regression enables sample-specific pan-cancer analysis. Bioinformatics. 34(13). i178–i186. 6 indexed citations

12.

Zheng, Xun, Bryon Aragam, Pradeep Ravikumar, & Eric P. Xing. (2018). DAGs with NO TEARS: Smooth Optimization for Structure Learning.. arXiv (Cornell University). 1 indexed citations

13.

Chen, Dan, et al.. (2018). The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. Neural Information Processing Systems. 31. 9321–9332. 1 indexed citations

14.

Wang, Haohan, Bryon Aragam, & Eric P. Xing. (2018). Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. Methods. 145. 2–9. 10 indexed citations

15.

Wang, Haohan, Benjamin J. Lengerich, Bryon Aragam, & Eric P. Xing. (2018). Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinformatics. 35(7). 1181–1187. 123 indexed citations

16.

Zheng, Xun, Bryon Aragam, Pradeep Ravikumar, & Eric P. Xing. (2018). DAGs with NO TEARS: Continuous Optimization for Structure Learning. arXiv (Cornell University). 31. 9472–9483. 85 indexed citations

17.

Aragam, Bryon, et al.. (2018). Fault Tolerance in Iterative-Convergent Machine Learning. arXiv (Cornell University). 5220–5230. 7 indexed citations

18.

Aragam, Bryon, et al.. (2017). Partial correlation graphs and the neighborhood lattice.. arXiv (Cornell University). 1 indexed citations

19.

Wang, Haohan, Bryon Aragam, & Eric P. Xing. (2017). Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. PubMed. 2017. 431–438. 19 indexed citations

20.

Aragam, Bryon & Qing Zhou. (2014). Concave Penalized Estimation of Sparse Bayesian Networks.. arXiv (Cornell University).

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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