Aurélien Lemay

718 total citations
12 papers, 94 citations indexed

About

Aurélien Lemay is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Aurélien Lemay has authored 12 papers receiving a total of 94 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Computational Theory and Mathematics and 3 papers in Computer Networks and Communications. Recurrent topics in Aurélien Lemay's work include Machine Learning and Algorithms (8 papers), semigroups and automata theory (5 papers) and Algorithms and Data Compression (5 papers). Aurélien Lemay is often cited by papers focused on Machine Learning and Algorithms (8 papers), semigroups and automata theory (5 papers) and Algorithms and Data Compression (5 papers). Aurélien Lemay collaborates with scholars based in France, Netherlands and Australia. Aurélien Lemay's co-authors include François Denis, Joachim Niehren, Rémi Gilleron, Sebastian Maneth, Radu Ciucanu, George Fletcher, Angela Bonifati, Michel Latteux, Joachim Niehren and Pierre Dupont and has published in prestigious journals such as Machine Learning, Journal of Machine Learning Research and Proceedings of the VLDB Endowment.

In The Last Decade

Aurélien Lemay

10 papers receiving 90 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aurélien Lemay France	6	80	56	20	14	7	12	94
Paula Severi United Kingdom	7	121 1.5×	85 1.5×	18 0.9×	28 2.0×	8 1.1×	23	141
Luca Roversi Italy	7	120 1.5×	111 2.0×	11 0.6×	15 1.1×	6 0.9×	16	143
Hans de Nivelle Germany	5	121 1.5×	66 1.2×	12 0.6×	19 1.4×	3 0.4×	16	127
David Nowak Japan	5	55 0.7×	44 0.8×	8 0.4×	7 0.5×	7 1.0×	11	68
Kevin Millikin Denmark	8	139 1.7×	83 1.5×	19 0.9×	13 0.9×	12 1.7×	15	143
Andreas Lochbihler Switzerland	7	95 1.2×	27 0.5×	12 0.6×	40 2.9×	6 0.9×	27	122
Takayoshi Shoudai Japan	6	69 0.9×	41 0.7×	17 0.8×	10 0.7×	2 0.3×	32	91
Anders B. Sandholm Denmark	6	68 0.8×	50 0.9×	21 1.1×	18 1.3×	28 4.0×	10	95
Naoki Nishida Japan	7	106 1.3×	78 1.4×	10 0.5×	19 1.4×	15 2.1×	35	121
Michał Walicki Norway	8	111 1.4×	77 1.4×	17 0.8×	10 0.7×	9 1.3×	35	145

Countries citing papers authored by Aurélien Lemay

Since Specialization

Citations

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

Fields of papers citing papers by Aurélien Lemay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aurélien Lemay

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

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Bonifati, Angela, et al.. (2017). gMark: schema-driven generation of graphs and queries (extended abstract). TU/e Research Portal.

2.

Bonifati, Angela, et al.. (2016). Generating flexible workloads for graph databases. Proceedings of the VLDB Endowment. 9(13). 1457–1460. 8 indexed citations

3.

Bonifati, Angela, et al.. (2015). Controlling Diversity in Benchmarking Graph Databases. arXiv (Cornell University). 9(13). 1447–1460. 1 indexed citations

4.

Niehren, Joachim, et al.. (2013). Query induction with schema-guided pruning strategies. Journal of Machine Learning Research. 14(1). 927–964.

5.

Lemay, Aurélien, et al.. (2013). Approximate membership for regular languages modulo the edit distance. Theoretical Computer Science. 487. 37–49. 1 indexed citations

6.

Lemay, Aurélien, Sebastian Maneth, & Joachim Niehren. (2010). A learning algorithm for top-down XML transformations. 285–296. 14 indexed citations

7.

Gilleron, Rémi, et al.. (2009). Efficient inclusion checking for deterministic tree automata and XML Schemas. Information and Computation. 207(11). 1181–1208. 9 indexed citations

8.

Latteux, Michel, et al.. (2006). Identification of biRFSA languages. Theoretical Computer Science. 356(1-2). 212–223. 2 indexed citations

9.

Gilleron, Rémi, et al.. (2006). Interactive learning of node selecting tree transducer. Machine Learning. 66(1). 33–67. 13 indexed citations

10.

Denis, François, et al.. (2003). Learning regular languages using RFSAs. Theoretical Computer Science. 313(2). 267–294. 40 indexed citations

11.

Denis, François, et al.. (2002). Residual Finite State Automata. Fundamenta Informaticae. 51(4). 339–368. 5 indexed citations

12.

Lemay, Aurélien, et al.. (2002). Learning Probabilistic Residual Finite Automata. Digital Access to Libraries. 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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