Aonghus Lawlor

2.0k total citations
79 papers, 1.1k citations indexed

About

Aonghus Lawlor is a scholar working on Artificial Intelligence, Information Systems and Biomedical Engineering. According to data from OpenAlex, Aonghus Lawlor has authored 79 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 18 papers in Information Systems and 16 papers in Biomedical Engineering. Recurrent topics in Aonghus Lawlor's work include Recommender Systems and Techniques (18 papers), Theoretical and Computational Physics (14 papers) and Material Dynamics and Properties (13 papers). Aonghus Lawlor is often cited by papers focused on Recommender Systems and Techniques (18 papers), Theoretical and Computational Physics (14 papers) and Material Dynamics and Properties (13 papers). Aonghus Lawlor collaborates with scholars based in Ireland, United States and Italy. Aonghus Lawlor's co-authors include Barry Smyth, Kenneth A. Dawson, P. Tartaglia, Neil Hurley, Ηλίας Τράγος, Paolo De Gregorio, Emanuela Zaccarelli, Giuseppe Foffi, Ronan P. Killeen and Francesco Sciortino and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and SHILAP Revista de lepidopterología.

In The Last Decade

Aonghus Lawlor

73 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aonghus Lawlor Ireland 18 360 291 255 194 155 79 1.1k
P. R. Eastham Ireland 26 387 1.1× 68 0.2× 61 0.2× 365 1.9× 126 0.8× 65 1.9k
Weidong Zhang China 22 215 0.6× 298 1.0× 68 0.3× 69 0.4× 80 0.5× 171 1.7k
Yujie Fan China 14 264 0.7× 89 0.3× 246 1.0× 94 0.5× 67 0.4× 56 791
Yuan Zhang China 23 359 1.0× 286 1.0× 625 2.5× 69 0.4× 23 0.1× 89 1.7k
Weichung Wang Taiwan 22 342 0.9× 24 0.1× 74 0.3× 99 0.5× 25 0.2× 102 1.5k
Sungdong Kim South Korea 12 2.8k 7.8× 171 0.6× 213 0.8× 72 0.4× 19 0.1× 42 4.0k
Stefan Harrer Australia 19 251 0.7× 249 0.9× 20 0.1× 525 2.7× 14 0.1× 52 1.7k
N. Shanthi India 18 159 0.4× 476 1.6× 46 0.2× 35 0.2× 379 2.4× 82 1.2k
David Wingate United States 15 438 1.2× 90 0.3× 34 0.1× 162 0.8× 39 0.3× 46 1.0k
Xiaocong Zhou China 13 142 0.4× 179 0.6× 138 0.5× 46 0.2× 58 0.4× 72 755

Countries citing papers authored by Aonghus Lawlor

Since Specialization
Citations

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

Fields of papers citing papers by Aonghus Lawlor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aonghus Lawlor

This figure shows the co-authorship network connecting the top 25 collaborators of Aonghus Lawlor. A scholar is included among the top collaborators of Aonghus Lawlor 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 Aonghus Lawlor. Aonghus Lawlor 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
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Lawlor, Aonghus, et al.. (2024). Automatic muscle segmentation on healthy abdominal MRI using nnUNet. SPIRE - Sciences Po Institutional REpository.
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Doherty, Cailbhe, R. Lambe, Barry Smyth, et al.. (2024). An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial. JMIR mhealth and uhealth. 12. e49443–e49443. 6 indexed citations
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Kelly, Brendan S., et al.. (2023). Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data. European Radiology. 33(11). 8376–8386. 3 indexed citations
7.
Τράγος, Ηλίας, et al.. (2023). Scalable Deep Q-Learning for Session-Based Slate Recommendation. 877–882. 3 indexed citations
8.
Lawlor, Aonghus, et al.. (2023). Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. SHILAP Revista de lepidopterología. 36. 3 indexed citations
9.
Kelly, Brendan S., Huy M., Yuhao Huang, et al.. (2023). DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy. European Radiology. 33(8). 5728–5739. 6 indexed citations
10.
Wang, Qinqin, Ηλίας Τράγος, Neil Hurley, et al.. (2022). Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems. IEEE Access. 10. 71961–71972. 1 indexed citations
11.
Wang, Qinqin, Ηλίας Τράγος, Neil Hurley, et al.. (2022). Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation. 79–88. 1 indexed citations
12.
McCarthy, Nicholas, et al.. (2021). Enterprise imaging and big data: A review from a medical physics perspective. Physica Medica. 83. 206–220. 6 indexed citations
13.
Τράγος, Ηλίας, Makbule Gülçin Özsoy, Ruihai Dong, et al.. (2021). DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning. IEEE Access. 9. 83340–83354. 5 indexed citations
14.
Lawlor, Aonghus, et al.. (2021). Improving Explainable Recommendations by Deep Review-Based Explanations. IEEE Access. 9. 67444–67455. 4 indexed citations
15.
Smyth, Barry, et al.. (2021). Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners. User Modeling and User-Adapted Interaction. 32(5). 787–838. 14 indexed citations
16.
Özsoy, Makbule Gülçin, Panagiotis Symeonidis, Ηλίας Τράγος, et al.. (2020). MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks. IEEE Access. 8. 181835–181847. 9 indexed citations
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Lawlor, Aonghus, et al.. (2017). Running with Recommendation.. Conference on Recommender Systems. 18–21. 6 indexed citations
18.
Lawlor, Aonghus, et al.. (2016). On the Use of Opinionated Explanations to Rank and Justify Recommendations. Arrow@dit (Dublin Institute of Technology). 554–559. 3 indexed citations
19.
Cellai, Davide, Aonghus Lawlor, Kenneth A. Dawson, & James P. Gleeson. (2011). Tricritical Point in Heterogeneousk-Core Percolation. Physical Review Letters. 107(17). 175703–175703. 42 indexed citations
20.
Dawson, Kenneth A., Paolo De Gregorio, & Aonghus Lawlor. (2006). Dynamically arrested states of matter. Advances in Colloid and Interface Science. 122(1-3). 35–38. 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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2026