Saminda Abeyruwan

432 total citations
10 papers, 226 citations indexed

About

Saminda Abeyruwan is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Saminda Abeyruwan has authored 10 papers receiving a total of 226 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 3 papers in Artificial Intelligence and 3 papers in Computational Theory and Mathematics. Recurrent topics in Saminda Abeyruwan's work include Biomedical Text Mining and Ontologies (4 papers), Computational Drug Discovery Methods (3 papers) and Bioinformatics and Genomic Networks (3 papers). Saminda Abeyruwan is often cited by papers focused on Biomedical Text Mining and Ontologies (4 papers), Computational Drug Discovery Methods (3 papers) and Bioinformatics and Genomic Networks (3 papers). Saminda Abeyruwan collaborates with scholars based in United States. Saminda Abeyruwan's co-authors include Ubbo Visser, Vance Lemmon, Uma D. Vempati, Stephan C. Schürer, Robin P. Smith, Kunie Sakurai, Caty Chung, Dilip Sarkar, Magdalena J. Przydzial and Joshua A. Bittker and has published in prestigious journals such as PLoS ONE, BMC Bioinformatics and IEEE Sensors Journal.

In The Last Decade

Saminda Abeyruwan

10 papers receiving 218 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saminda Abeyruwan United States	7	144	108	40	21	19	10	226
René Rahn Germany	5	145 1.0×	34 0.3×	67 1.7×	16 0.8×	6 0.3×	6	235
Tomasz Danel Poland	8	142 1.0×	191 1.8×	32 0.8×	12 0.6×	25 1.3×	16	304
Łukasz Maziarka Poland	5	105 0.7×	147 1.4×	27 0.7×	12 0.6×	16 0.8×	11	213
Marco Donizelli United Kingdom	4	370 2.6×	42 0.4×	16 0.4×	17 0.8×	36 1.9×	8	445
Ulrike Wittig Germany	13	504 3.5×	59 0.5×	31 0.8×	9 0.4×	65 3.4×	25	577
Luca Tesei Italy	10	115 0.8×	136 1.3×	48 1.2×	15 0.7×	25 1.3×	43	280
Brian Belgodere United States	5	105 0.7×	144 1.3×	52 1.3×	4 0.2×	7 0.4×	7	289
Marco Capuccini Sweden	6	49 0.3×	40 0.4×	37 0.9×	12 0.6×	7 0.4×	8	144
Jerret Ross United States	4	104 0.7×	147 1.4×	46 1.1×	4 0.2×	7 0.4×	10	250
André Nascimento Brazil	8	160 1.1×	153 1.4×	73 1.8×	2 0.1×	5 0.3×	40	320

Countries citing papers authored by Saminda Abeyruwan

Since Specialization

Citations

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

Fields of papers citing papers by Saminda Abeyruwan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saminda Abeyruwan

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

All Works

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10 of 10 papers shown

1.

Ding, Tianli, Saminda Abeyruwan, David B. D’Ambrosio, et al.. (2022). Learning High Speed Precision Table Tennis on a Physical Robot. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10780–10787. 9 indexed citations

2.

Callahan, Alison, Saminda Abeyruwan, Hassan Al‐Ali, et al.. (2016). RegenBase: a knowledge base of spinal cord injury biology for translational research. Database. 2016. baw040–baw040. 8 indexed citations

3.

Abeyruwan, Saminda, et al.. (2016). Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring. IEEE Sensors Journal. 16(13). 5406–5415. 17 indexed citations

4.

Abeyruwan, Saminda, et al.. (2015). Activity monitoring and prediction for humans and NAO humanoid robots using wearable sensors. 342–347. 4 indexed citations

5.

Lemmon, Vance, Saminda Abeyruwan, Ubbo Visser, & John L. Bixby. (2014). Facilitating transparency in spinal cord injury studies using data standards and ontologies. Neural Regeneration Research. 9(1). 6–6. 8 indexed citations

6.

Abeyruwan, Saminda, Uma D. Vempati, Ubbo Visser, et al.. (2014). Evolving BioAssay Ontology (BAO): modularization, integration and applications. Journal of Biomedical Semantics. 5(S1). S5–S5. 56 indexed citations

7.

Abeyruwan, Saminda, et al.. (2014). Humanoid Robots and Spoken Dialog Systems for Brief Health Interventions. 2–4. 1 indexed citations

8.

Abeyruwan, Saminda, et al.. (2013). Dynamic role assignment using general value functions. 1 indexed citations

9.

Vempati, Uma D., Magdalena J. Przydzial, Caty Chung, et al.. (2012). Formalization, Annotation and Analysis of Diverse Drug and Probe Screening Assay Datasets Using the BioAssay Ontology (BAO). PLoS ONE. 7(11). e49198–e49198. 35 indexed citations

10.

Visser, Ubbo, Saminda Abeyruwan, Uma D. Vempati, et al.. (2011). BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results. BMC Bioinformatics. 12(1). 257–257. 87 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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