Onureena Banerjee

1.9k total citations · 1 hit paper
5 papers, 1.0k citations indexed

About

Onureena Banerjee is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Onureena Banerjee has authored 5 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Statistics and Probability, 3 papers in Artificial Intelligence and 2 papers in Molecular Biology. Recurrent topics in Onureena Banerjee's work include Statistical Methods and Inference (5 papers), Bayesian Modeling and Causal Inference (2 papers) and Machine Learning and Algorithms (2 papers). Onureena Banerjee is often cited by papers focused on Statistical Methods and Inference (5 papers), Bayesian Modeling and Causal Inference (2 papers) and Machine Learning and Algorithms (2 papers). Onureena Banerjee collaborates with scholars based in United States and France. Onureena Banerjee's co-authors include Alexandre d’Aspremont, Laurent El Ghaoui, Georges Natsoulis, Éric Jougla, Joël Coste, Vivian Viallon and Grégoire Rey and has published in prestigious journals such as Journal of Machine Learning Research, SIAM Journal on Matrix Analysis and Applications and Biometrical Journal.

In The Last Decade

Onureena Banerjee

5 papers receiving 969 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data

2008 732 citations Onureena Banerjee, Laurent El Ghaoui et al. Journal of Machine Learning Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Onureena Banerjee United States	4	472	423	239	222	93	5	1.0k
Pradeep Ravikumar United States	9	454 1.0×	359 0.8×	178 0.7×	122 0.5×	56 0.6×	17	846
Gilles Blanchard Germany	19	225 0.5×	561 1.3×	133 0.6×	77 0.3×	117 1.3×	53	1.1k
Mátyás A. Sustik United States	7	150 0.3×	272 0.6×	233 1.0×	63 0.3×	125 1.3×	10	749
Cun-Hui Zhang United States	11	449 1.0×	194 0.5×	79 0.3×	104 0.5×	43 0.5×	16	825
C. Eric United States	13	125 0.3×	279 0.7×	147 0.6×	130 0.6×	71 0.8×	41	743
Yunzhang Zhu United States	10	220 0.5×	174 0.4×	117 0.5×	75 0.3×	29 0.3×	19	492
Venkat Chandrasekaran United States	11	121 0.3×	184 0.4×	214 0.9×	41 0.2×	90 1.0×	27	589
Haipeng Shen United States	9	152 0.3×	143 0.3×	183 0.8×	192 0.9×	144 1.5×	23	757
Rodolphe Jenatton France	14	129 0.3×	353 0.8×	523 2.2×	80 0.4×	119 1.3×	19	1.1k
Choon Hui Teo United States	13	168 0.4×	609 1.4×	79 0.3×	56 0.3×	112 1.2×	16	916

Countries citing papers authored by Onureena Banerjee

Since Specialization

Citations

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

Fields of papers citing papers by Onureena Banerjee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Onureena Banerjee

This figure shows the co-authorship network connecting the top 25 collaborators of Onureena Banerjee. A scholar is included among the top collaborators of Onureena Banerjee 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 Onureena Banerjee. Onureena Banerjee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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5 of 5 papers shown

1.

Viallon, Vivian, Onureena Banerjee, Éric Jougla, Grégoire Rey, & Joël Coste. (2013). Empirical comparison study of approximate methods for structure selection in binary graphical models. Biometrical Journal. 56(2). 307–331. 4 indexed citations

2.

Banerjee, Onureena, Laurent El Ghaoui, & Alexandre d’Aspremont. (2008). Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. Journal of Machine Learning Research. 9(15). 485–516. 732 indexed citations breakdown →

3.

d’Aspremont, Alexandre, Onureena Banerjee, & Laurent El Ghaoui. (2008). First-Order Methods for Sparse Covariance Selection. SIAM Journal on Matrix Analysis and Applications. 30(1). 56–66. 204 indexed citations

4.

Banerjee, Onureena, Laurent El Ghaoui, & Alexandre d’Aspremont. (2007). Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. arXiv (Cornell University). 1 indexed citations

5.

Banerjee, Onureena, Laurent El Ghaoui, Alexandre d’Aspremont, & Georges Natsoulis. (2006). Convex optimization techniques for fitting sparse Gaussian graphical models. 89–96. 94 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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