Abhishek Kaul

571 total citations
11 papers, 240 citations indexed

About

Abhishek Kaul is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Abhishek Kaul has authored 11 papers receiving a total of 240 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Statistics and Probability, 4 papers in Artificial Intelligence and 3 papers in Molecular Biology. Recurrent topics in Abhishek Kaul's work include Statistical Methods and Inference (8 papers), Bayesian Methods and Mixture Models (4 papers) and Gut microbiota and health (3 papers). Abhishek Kaul is often cited by papers focused on Statistical Methods and Inference (8 papers), Bayesian Methods and Mixture Models (4 papers) and Gut microbiota and health (3 papers). Abhishek Kaul collaborates with scholars based in United States, Israel and Norway. Abhishek Kaul's co-authors include Shyamal D. Peddada, Ori Davidov, Siddhartha Mandal, Hira L. Koul, Venkata K. Jandhyala, S. Fotopoulos, Abolfazl Safikhani, Francisco Gómez Real, Tamar Ringel‐Kulka and Shyamal Peddada and has published in prestigious journals such as Frontiers in Microbiology, Epidemiology and Journal of Machine Learning Research.

In The Last Decade

Abhishek Kaul

11 papers receiving 238 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Abhishek Kaul United States	4	159	31	29	24	21	11	240
Courtney R. Armour United States	6	227 1.4×	43 1.4×	26 0.9×	14 0.6×	3 0.1×	9	299
Anna Paola Carrieri United Kingdom	9	230 1.4×	33 1.1×	43 1.5×	41 1.7×	2 0.1×	18	382
Stephen Woloszynek United States	11	166 1.0×	17 0.5×	59 2.0×	13 0.5×	3 0.1×	13	339
Adelaide Freitas Portugal	6	183 1.2×	10 0.3×	21 0.7×	7 0.3×	8 0.4×	29	278
Michelle Badri United States	5	175 1.1×	24 0.8×	45 1.6×	7 0.3×	2 0.1×	5	277
Elijah Bogart United States	4	271 1.7×	24 0.8×	35 1.2×	14 0.6×	3 0.1×	6	354
Sicheng Wu China	8	282 1.8×	39 1.3×	31 1.1×	16 0.7×		12	374
Morgan Essex Germany	4	143 0.9×	24 0.8×	16 0.6×	9 0.4×		5	182
Dmitriy Babenko Kazakhstan	10	126 0.8×	53 1.7×	16 0.6×	13 0.5×		41	322

Countries citing papers authored by Abhishek Kaul

Since Specialization

Citations

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

Fields of papers citing papers by Abhishek Kaul

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abhishek Kaul

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

All Works

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11 of 11 papers shown

Kaul, Abhishek, et al.. (2024). Inference on Multiple Change Points in High Dimensional Linear Regression Models. Econometrics and Statistics. 3 indexed citations

Lee, Mi Kyeong, Abhishek Kaul, James M. Ward, et al.. (2024). House dust metagenome and pulmonary function in a US farming population. Microbiome. 12(1). 129–129. 1 indexed citations

Kaul, Abhishek & George Michailidis. (2023). Inference for Change Points in High Dimensional Mean Shift Models. Statistica Sinica. 2 indexed citations

Kaul, Abhishek, S. Fotopoulos, Venkata K. Jandhyala, & Abolfazl Safikhani. (2021). Inference on the change point under a high dimensional sparse mean shift. Electronic Journal of Statistics. 15(1). 7 indexed citations

Kaul, Abhishek, Venkata K. Jandhyala, & S. Fotopoulos. (2019). An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search. Journal of Machine Learning Research. 20(111). 1–40. 1 indexed citations

MacNeill, Ian B., Venkata K. Jandhyala, Abhishek Kaul, & S. Fotopoulos. (2019). Multiple change‐point models for time series. Environmetrics. 31(1). 1 indexed citations

Kaul, Abhishek, et al.. (2017). Structural zeros in high-dimensional data with applications to microbiome studies. Biostatistics. 18(3). kxw053–kxw053. 16 indexed citations

Kaul, Abhishek, Siddhartha Mandal, Ori Davidov, & Shyamal D. Peddada. (2017). Analysis of Microbiome Data in the Presence of Excess Zeros. Frontiers in Microbiology. 8. 2114–2114. 199 indexed citations

Bertelsen, Randi Jacobsen, Tamar Ringel‐Kulka, Shyamal Peddada, et al.. (2017). Oral microbiome and associations with chemical exposure, asthma and lung function. Epidemiology. OA321–OA321. 3 indexed citations

10.

Kaul, Abhishek & Hira L. Koul. (2015). Weighted ℓ1-penalized corrected quantile regression for high dimensional measurement error models. Journal of Multivariate Analysis. 140. 72–91. 4 indexed citations

11.

Kaul, Abhishek. (2014). Lasso with long memory regression errors. Journal of Statistical Planning and Inference. 153. 11–26. 3 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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