Robin Mitra

1.1k total citations
35 papers, 694 citations indexed

About

Robin Mitra is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Robin Mitra has authored 35 papers receiving a total of 694 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Statistics and Probability, 12 papers in Artificial Intelligence and 11 papers in Molecular Biology. Recurrent topics in Robin Mitra's work include Statistical Methods and Bayesian Inference (12 papers), Statistical Methods and Inference (11 papers) and Biochemical Acid Research Studies (9 papers). Robin Mitra is often cited by papers focused on Statistical Methods and Bayesian Inference (12 papers), Statistical Methods and Inference (11 papers) and Biochemical Acid Research Studies (9 papers). Robin Mitra collaborates with scholars based in United Kingdom, United States and India. Robin Mitra's co-authors include Jerome P. Reiter, John M. Woodley, M. D. Lilly, Nicholas J. Turner, Katie Morris, Mark E. B. Smith, Stefanie Biedermann, David B. Dunson, Anahid Basiri and Sarah F. McGough and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and Annals of the New York Academy of Sciences.

In The Last Decade

Robin Mitra

35 papers receiving 677 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robin Mitra United Kingdom 13 168 143 143 82 59 35 694
Keewhan Choi United States 16 143 0.9× 30 0.2× 140 1.0× 69 0.8× 1 0.0× 22 994
Frantz Thiessard France 20 151 0.9× 15 0.1× 130 0.9× 88 1.1× 60 1.2k
Bhawna Singh India 15 206 1.2× 7 0.0× 24 0.2× 12 0.1× 4 0.1× 46 1.1k
Prakash Joshi India 13 31 0.2× 5 0.0× 175 1.2× 43 0.5× 5 0.1× 59 631
Michael Dickson United States 14 215 1.3× 2 0.0× 28 0.2× 50 0.6× 39 0.7× 56 1.0k
Xavier Fuentes‐Arderiu Spain 19 93 0.6× 5 0.0× 121 0.8× 38 0.5× 3 0.1× 80 998
James D. Cowan United States 9 179 1.1× 8 0.1× 49 0.3× 102 1.2× 12 739
Arden W. Forrey United States 9 313 1.9× 24 0.2× 11 0.1× 163 2.0× 11 687
Yawen Jiang China 18 204 1.2× 4 0.0× 21 0.1× 33 0.4× 59 1.0× 92 992
Kavita Venkataraman Singapore 20 82 0.5× 3 0.0× 35 0.2× 66 0.8× 4 0.1× 69 1.2k

Countries citing papers authored by Robin Mitra

Since Specialization
Citations

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

Fields of papers citing papers by Robin Mitra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robin Mitra

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

All Works

20 of 20 papers shown
1.
Chakraborti, Tapabrata, Christopher R. S. Banerji, Robin Mitra, et al.. (2025). Personalized uncertainty quantification in artificial intelligence. Nature Machine Intelligence. 7(4). 522–530. 6 indexed citations
2.
Mitra, Robin, et al.. (2023). Sirolimus for Pediatric Cervicofacial Lymphatic Malformation: A Systematic Review and Meta‐Analysis. The Laryngoscope. 134(5). 2038–2047. 5 indexed citations
3.
Mitra, Robin, Sarah F. McGough, Tapabrata Chakraborti, et al.. (2023). Learning from data with structured missingness. Nature Machine Intelligence. 5(1). 13–23. 30 indexed citations
4.
Stavola, Bianca De, et al.. (2022). An overview on synthetic administrative data for research. International Journal for Population Data Science. 7(1). 1727–1727. 12 indexed citations
5.
6.
Jackson, James F., et al.. (2022). Using Saturated Count Models for User-Friendly Synthesis of Large Confidential Administrative Databases. Journal of the Royal Statistical Society Series A (Statistics in Society). 185(4). 1613–1643. 4 indexed citations
7.
Biedermann, Stefanie, et al.. (2019). D‐optimal designs for multiarm trials with dropouts. Statistics in Medicine. 38(15). 2749–2766. 2 indexed citations
8.
Biedermann, Stefanie, et al.. (2017). Optimal design for experiments with possibly incomplete observations. Statistica Sinica. 4 indexed citations
9.
Mitra, Robin, et al.. (2015). Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models. Computational Statistics & Data Analysis. 95. 24–38. 11 indexed citations
10.
Mitra, Robin, et al.. (2015). Using mixtures oftdensities to make inferences in the presence of missing data with a small number of multiply imputed data sets. Computational Statistics & Data Analysis. 92. 84–96. 1 indexed citations
11.
Hu, Jingchen, Robin Mitra, & Jerome P. Reiter. (2013). Are Independent Parameter Draws Necessary for Multiple Imputation?. The American Statistician. 67(3). 143–149. 4 indexed citations
12.
Mitra, Robin & Jerome P. Reiter. (2011). Propensity score matching with missing covariatesvia iterated, sequential multiple imputation. ePrints Soton (University of Southampton). 1 indexed citations
13.
Mitra, Robin & David B. Dunson. (2010). Two-Level Stochastic Search Variable Selection in GLMs with Missing Predictors. The International Journal of Biostatistics. 6(1). Article 33–Article 33. 12 indexed citations
14.
Mitra, Robin & Jerome P. Reiter. (2010). Estimating propensity scores with missing covariate data using general location mixture models. Statistics in Medicine. 30(6). 627–641. 30 indexed citations
15.
Lalloo, Rajesh, et al.. (2008). Multi-copy expression and fed-batch production of Rhodotorula araucariae epoxide hydrolase in Yarrowia lipolytica. Applied Microbiology and Biotechnology. 79(2). 235–244. 8 indexed citations
16.
Mitra, Robin, John M. Woodley, & M. D. Lilly. (1998). Escherichia coli transketolase-catalyzed carbon-carbon bond formation: biotransformation characterization for reactor evaluation and selection. Enzyme and Microbial Technology. 22(1). 64–70. 39 indexed citations
17.
Mitra, Robin, et al.. (1996). Enzyme-catalysed carbon-carbon bond formation: Large-scale production of Escherichia coli transketolase. Journal of Biotechnology. 45(2). 173–179. 31 indexed citations
18.
Lilly, M. D., Clare E. French, A. Humphrey, et al.. (1996). Carbon–Carbon Bond Synthesis: The Impact of rDNA Technology on the Production and Use of E. coli Transketolase. Annals of the New York Academy of Sciences. 782(1). 513–525. 20 indexed citations
19.
Mitra, Robin, et al.. (1996). Carbon‐Carbon Bond Synthesis. Annals of the New York Academy of Sciences. 799(1). 729–736. 5 indexed citations
20.
Mitra, Robin, et al.. (1975). On Schwarz canonical form for large system simplification. IEEE Transactions on Automatic Control. 20(2). 262–263. 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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