Ajitha Rajan

1.9k total citations
53 papers, 684 citations indexed

About

Ajitha Rajan is a scholar working on Software, Artificial Intelligence and Information Systems. According to data from OpenAlex, Ajitha Rajan has authored 53 papers receiving a total of 684 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Software, 17 papers in Artificial Intelligence and 16 papers in Information Systems. Recurrent topics in Ajitha Rajan's work include Software Testing and Debugging Techniques (25 papers), Software Reliability and Analysis Research (11 papers) and Software Engineering Research (9 papers). Ajitha Rajan is often cited by papers focused on Software Testing and Debugging Techniques (25 papers), Software Reliability and Analysis Research (11 papers) and Software Engineering Research (9 papers). Ajitha Rajan collaborates with scholars based in United Kingdom, United States and Canada. Ajitha Rajan's co-authors include Mats P. E. Heimdahl, Michael W. Whalen, Steven P. Miller, Chao Peng, Thomas Wahl, Matt Staats, Christophe Dubach, Renée Bryce, Subodh Sharma and Peter Schrammel and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer Research and Protein Engineering Design and Selection.

In The Last Decade

Ajitha Rajan

46 papers receiving 647 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ajitha Rajan United Kingdom 12 413 373 179 170 82 53 684
Daniel Ratiu Germany 13 453 1.1× 524 1.4× 319 1.8× 153 0.9× 57 0.7× 52 765
Muzammil Shahbaz United Kingdom 9 663 1.6× 422 1.1× 201 1.1× 165 1.0× 44 0.5× 15 839
Yong Rae Kwon South Korea 13 794 1.9× 646 1.7× 154 0.9× 146 0.9× 77 0.9× 35 956
Markus Lumpe Australia 10 309 0.7× 455 1.2× 224 1.3× 161 0.9× 22 0.3× 32 598
Martin R. Woodward United Kingdom 13 703 1.7× 478 1.3× 153 0.9× 122 0.7× 87 1.1× 39 852
Giovanni Denaro Italy 17 726 1.8× 673 1.8× 230 1.3× 336 2.0× 52 0.6× 68 960
Paul Strooper Australia 19 772 1.9× 505 1.4× 232 1.3× 191 1.1× 117 1.4× 108 998
Brian A. Malloy United States 16 483 1.2× 561 1.5× 311 1.7× 179 1.1× 103 1.3× 84 798
W.B. Mugridge New Zealand 11 566 1.4× 495 1.3× 235 1.3× 160 0.9× 103 1.3× 35 849
Edward Aftandilian United States 12 327 0.8× 464 1.2× 202 1.1× 205 1.2× 90 1.1× 17 670

Countries citing papers authored by Ajitha Rajan

Since Specialization
Citations

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

Fields of papers citing papers by Ajitha Rajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ajitha Rajan

This figure shows the co-authorship network connecting the top 25 collaborators of Ajitha Rajan. A scholar is included among the top collaborators of Ajitha Rajan 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 Ajitha Rajan. Ajitha Rajan 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.
Oyarzún, Diego A., et al.. (2025). Tuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization. Protein Engineering Design and Selection. 38. 3 indexed citations
2.
Rajan, Ajitha, et al.. (2025). A novel decoding strategy for ProteinMPNN to design with less visibility to cytotoxic T-lymphocytes. Computational and Structural Biotechnology Journal. 27. 3693–3703.
3.
Oyarzún, Diego A., et al.. (2024). Guiding a language-model based protein design method towards MHC Class-I immune-visibility targets in vaccines and therapeutics. SHILAP Revista de lepidopterología. 14. 100035–100035. 3 indexed citations
4.
Wulf, W., et al.. (2024). Knowledge graph embeddings in the biomedical domain: are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks. Bioinformatics Advances. 4(1). vbae097–vbae097. 3 indexed citations
5.
Rajan, Ajitha, et al.. (2024). Building trust in deep learning-based immune response predictors with interpretable explanations. Communications Biology. 7(1). 279–279. 3 indexed citations
6.
Brennan, Paul, et al.. (2023). Challenges in Explaining Brain Tumor Detection. 1–8. 3 indexed citations
7.
Bedran, Georges, Cátia Pesquita, Daniel Faria, et al.. (2023). Abstract 6577: CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy. Cancer Research. 83(7_Supplement). 6577–6577. 1 indexed citations
8.
Cano, José, et al.. (2023). Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1795–1799. 3 indexed citations
9.
Rajan, Ajitha, et al.. (2021). Embedding and classifying test execution traces using neural networks. IET Software. 16(3). 301–316. 4 indexed citations
10.
Gay, Gregory, Ajitha Rajan, Matt Staats, Michael W. Whalen, & Mats P. E. Heimdahl. (2016). The Effect of Program and Model Structure on the Effectiveness of MC/DC Test Adequacy Coverage. ACM Transactions on Software Engineering and Methodology. 25(3). 1–34. 21 indexed citations
11.
Rajan, Ajitha. (2016). Web sentiment analysis. International journal of applied research. 2(5). 563–566. 1 indexed citations
12.
Noureddine, Adel & Ajitha Rajan. (2015). Optimising Energy Consumption of Design Patterns. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. 623–626. 2 indexed citations
13.
Rajan, Ajitha. (2013). A STUDY ON SECURITY THREAT AWARENESS AMONG STUDENTS USING SOCIAL NETWORKING SITES, BY APPLYING DATA MINING TECHNIQUES. International Journal of Research in Commerce, IT and Management. 1 indexed citations
14.
Rajan, Ajitha, et al.. (2012). PINCETTE - Validating Changes and Upgrades in Networked Software. ERCIM news/ERCIM news online edition. 2012(88). 1 indexed citations
15.
Staats, Matt, Michael W. Whalen, Ajitha Rajan, & Mats P. E. Heimdahl. (2010). Coverage Metrics for Requirements-Based Testing: Evaluation of Effectiveness. University of Minnesota Digital Conservancy (University of Minnesota). 161–170. 11 indexed citations
16.
Staats, Matt, et al.. (2008). 23rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2008), 15-19 September 2008, L'Aquila, Italy. University of Minnesota Digital Conservancy (University of Minnesota). 3 indexed citations
17.
Rajan, Ajitha, Michael W. Whalen, & Mats P. E. Heimdahl. (2008). Proceedings of the 30th international conference on Software engineering. 244 indexed citations
18.
Rajan, Ajitha, Michael W. Whalen, & Mats P. E. Heimdahl. (2008). The effect of program and model structure on mc/dc test adequacy coverage. 161–170. 50 indexed citations
19.
Heimdahl, Mats P. E., Michael W. Whalen, Ajitha Rajan, & Matt Staats. (2008). On MC/DC and implementation structure: An empirical study. University of Minnesota Digital Conservancy (University of Minnesota). 5.B.3–1. 16 indexed citations
20.
Staats, Matt, et al.. (2008). ReqsCov: A Tool for Measuring Test-Adequacy over Requirements. 9. 499–500. 1 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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