Abhyuday Mandal

846 total citations
37 papers, 486 citations indexed

About

Abhyuday Mandal is a scholar working on Management Science and Operations Research, Computational Theory and Mathematics and Statistics and Probability. According to data from OpenAlex, Abhyuday Mandal has authored 37 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Management Science and Operations Research, 17 papers in Computational Theory and Mathematics and 10 papers in Statistics and Probability. Recurrent topics in Abhyuday Mandal's work include Optimal Experimental Design Methods (18 papers), Advanced Multi-Objective Optimization Algorithms (16 papers) and Statistical Methods and Bayesian Inference (6 papers). Abhyuday Mandal is often cited by papers focused on Optimal Experimental Design Methods (18 papers), Advanced Multi-Objective Optimization Algorithms (16 papers) and Statistical Methods and Bayesian Inference (6 papers). Abhyuday Mandal collaborates with scholars based in United States, China and India. Abhyuday Mandal's co-authors include Gauri Sankar Datta, Jie Yang, Suraj Sharma, John Stufken, Alexander Jones, Weng Kee Wong, Peter Hall, Nicole A. Lazar, Joshua Lukemire and Dibyen Majumdar and has published in prestigious journals such as Journal of the American Statistical Association, NeuroImage and Technometrics.

In The Last Decade

Abhyuday Mandal

34 papers receiving 470 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Abhyuday Mandal United States 13 165 129 112 55 53 37 486
Neil A. Butler United Kingdom 12 305 1.8× 88 0.7× 210 1.9× 19 0.3× 5 0.1× 35 483
Sanjay Kumar Tyagi India 12 181 1.1× 45 0.3× 60 0.5× 71 1.3× 30 0.6× 38 421
Zichun Chen China 16 256 1.6× 76 0.6× 75 0.7× 76 1.4× 1 0.0× 40 866
Łukasz Smaga Poland 9 75 0.5× 143 1.1× 23 0.2× 56 1.0× 6 0.1× 50 322
Julio L. Peixoto United States 8 52 0.3× 106 0.8× 27 0.2× 44 0.8× 3 0.1× 15 339
Guanqun Cao United States 12 20 0.1× 177 1.4× 19 0.2× 124 2.3× 4 0.1× 36 437
Cuthbert Daniel 3 161 1.0× 136 1.1× 45 0.4× 19 0.3× 11 0.2× 6 432
Lihong Li China 12 49 0.3× 16 0.1× 16 0.1× 110 2.0× 14 0.3× 52 538
Qianqian Zhao China 12 32 0.2× 13 0.1× 48 0.4× 102 1.9× 14 0.3× 36 424
Souvik Das India 11 158 1.0× 69 0.5× 53 0.5× 34 0.6× 3 0.1× 31 423

Countries citing papers authored by Abhyuday Mandal

Since Specialization
Citations

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

Fields of papers citing papers by Abhyuday Mandal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abhyuday Mandal

This figure shows the co-authorship network connecting the top 25 collaborators of Abhyuday Mandal. A scholar is included among the top collaborators of Abhyuday Mandal 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 Abhyuday Mandal. Abhyuday Mandal 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.
2.
Zhang, Shufan, Yue Wu, Yinping Guo, et al.. (2024). Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology. Scientific Reports. 14(1). 10866–10866. 4 indexed citations
3.
Yang, Jie, et al.. (2023). A General Equivalence Theorem for Crossover Designs under Generalized Linear Models. Sankhya B. 85(2). 344–364. 1 indexed citations
4.
Xiao, Qian, Yaping Wang, Abhyuday Mandal, & Xinwei Deng. (2022). Modeling and Active Learning for Experiments with Quantitative-Sequence Factors. Journal of the American Statistical Association. 119(545). 407–421. 4 indexed citations
5.
Chuang, Yen‐Jun, et al.. (2021). Prenatal exposure to bisphenols affects pregnancy outcomes and offspring development in rats. Chemosphere. 276. 130118–130118. 34 indexed citations
6.
Meng, Cheng, Rui Xie, Abhyuday Mandal, et al.. (2020). LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model. Journal of Computational and Graphical Statistics. 30(3). 694–708. 34 indexed citations
7.
Mandal, Abhyuday, et al.. (2020). Optimal Crossover Designs for Generalized Linear Models. Journal of Statistical Theory and Practice. 14(2). 10 indexed citations
8.
Kinder, Holly A., et al.. (2020). Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model. Neural Regeneration Research. 16(2). 338–338. 9 indexed citations
9.
Mandal, Abhyuday, et al.. (2020). Microencapsulation of retinyl palmitate by melt dispersion for cosmetic application. Journal of Microencapsulation. 37(3). 205–219. 13 indexed citations
10.
Henriquez, Joseph E., Christian Laurent, Yen‐Jun Chuang, et al.. (2020). Independent and combined effects of Bisphenol A and Diethylhexyl Phthalate on gestational outcomes and offspring development in Sprague-Dawley rats. Chemosphere. 263. 128307–128307. 23 indexed citations
11.
Mandal, Abhyuday, et al.. (2020). Using Differential Evolution to design optimal experiments. Chemometrics and Intelligent Laboratory Systems. 199. 103955–103955. 15 indexed citations
12.
Lukemire, Joshua, Abhyuday Mandal, & Weng Kee Wong. (2018). d-QPSO: A Quantum-Behaved Particle Swarm Technique for FindingD-Optimal Designs With Discrete and Continuous Factors and a Binary Response. Technometrics. 61(1). 77–87. 24 indexed citations
13.
Jones, Alexander, Jitendra Pant, Marcus J. Goudie, et al.. (2018). Nitric oxide‐releasing antibacterial albumin plastic for biomedical applications. Journal of Biomedical Materials Research Part A. 106(6). 1535–1542. 5 indexed citations
14.
Datta, Gauri Sankar, et al.. (2016). A two component normal mixture alternative to the Fay-Herriot model. Statistics in Transition New Series. 17(1). 67–90.
15.
Yang, Jie, Abhyuday Mandal, & Dibyen Majumdar. (2015). Optimal designs for 2k factorial experiments with binary response. Statistica Sinica. 11 indexed citations
16.
Yang, Jie & Abhyuday Mandal. (2014). D-optimal Factorial Designs under Generalized Linear Models. Communications in Statistics - Simulation and Computation. 44(9). 2264–2277. 11 indexed citations
17.
Yang, Jie, Abhyuday Mandal, & Dibyen Majumdar. (2011). Optimal designs for two-level factorial experiments with binary response. Statistica Sinica. 22(2). 19 indexed citations
18.
Datta, Gauri Sankar, Peter Hall, & Abhyuday Mandal. (2011). Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data. Journal of the American Statistical Association. 106(493). 362–374. 44 indexed citations
19.
Johnson, Kjell, et al.. (2008). Software for Implementing the Sequential Elimination of Level Combinations Algorithm. Journal of Statistical Software. 25(6). 3 indexed citations
20.
Mandal, Abhyuday, et al.. (2008). Multi-objective optimal experimental designs for event-related fMRI studies. NeuroImage. 44(3). 849–856. 56 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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