Aonghus Lawlor

77 papers receiving 1.2k citations

Peers

Aonghus Lawlor
Comparison fields: 5 of 133
  • Health Informatics 105
  • Condensed Matter Physics 158
  • Information Systems 262
  • Artificial Intelligence 361
  • Orthopedics and Sports Medicine 65
Replace Giacomo Fiumara with:
Giacomo Fiumara Italy
Ke Hu China
Yujie Fan China
Hua Zheng China
Stephan Schneider Germany
David Wingate United States
Weichung Wang Taiwan
Nicholas Walker United States
Rachel Courtland United States
Yuan Zhang China
Aonghus Lawlor relative to Giacomo Fiumara Italy Giacomo Fiumara's profile →
Citations per field
00.5×20×40×65×
Giacomo Fiumara · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Aonghus Lawlor, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Aonghus Lawlor Line = papers co-authored together Aonghus Lawlor links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002157
2 2020147
3 2022144
4 201852
5 200248
6 201142
7 201931
8 200526
9 201625
10 202424
11 200423
12 201922
13 201920
14 200220
15 201320
16 202320
17 202019
18 201919
19 202118
20 201417

About Aonghus Lawlor

Aonghus Lawlor is a scholar working on Artificial Intelligence, Information Systems, Condensed Matter Physics, Materials Chemistry and Computer Vision and Pattern Recognition, having authored 79 papers that have together received 1.2k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (18 papers), Theoretical and Computational Physics (14 papers), Material Dynamics and Properties (13 papers), Topic Modeling (9 papers), Sports Performance and Training (8 papers), Artificial Intelligence in Healthcare and Education (7 papers), Phase Equilibria and Thermodynamics (5 papers) and Video Analysis and Summarization (4 papers). The work is most often cited by research in Health Informatics (105 citations), Condensed Matter Physics (158 citations), Information Systems (262 citations), Artificial Intelligence (361 citations) and Orthopedics and Sports Medicine (65 citations). Aonghus Lawlor has collaborated with scholars based in Ireland, United States and Italy. Frequent 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 Brendan S. Kelly. Their work appears in journals such as European Radiology, IEEE Access, Physical Review Letters, Physica A Statistical Mechanics and its Applications and Insights into Imaging.

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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