Mark van der Wilk

91 total papers · 1.7k total citations
18 papers, 317 citations indexed

About

Mark van der Wilk is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Automotive Engineering. According to data from OpenAlex, Mark van der Wilk has authored 18 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 5 papers in Computational Theory and Mathematics and 2 papers in Automotive Engineering. Recurrent topics in Mark van der Wilk's work include Gaussian Processes and Bayesian Inference (8 papers), Machine Learning and Data Classification (6 papers) and Advanced Multi-Objective Optimization Algorithms (5 papers). Mark van der Wilk is often cited by papers focused on Gaussian Processes and Bayesian Inference (8 papers), Machine Learning and Data Classification (6 papers) and Advanced Multi-Objective Optimization Algorithms (5 papers). Mark van der Wilk collaborates with scholars based in United Kingdom, Germany and United States. Mark van der Wilk's co-authors include Yarin Gal, Adrian Weller, Roberto Cipolla, Alex Kendall, Rowan McAllister, Anoop Shah, Carl Edward Rasmussen, Amar Shah, James Hensman and Ruth Misener and has published in prestigious journals such as Biotechnology and Bioengineering, Computers & Chemical Engineering and Software Impacts.

In The Last Decade

Mark van der Wilk

17 papers receiving 306 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Mark van der Wilk	170	101	80	51	35	18	317
Zachary N. Sunberg	185 1.1×	73 0.7×	107 1.3×	88 1.7×	14 0.4×	27	356
Martina Hasenjäger	79 0.5×	113 1.1×	43 0.5×	66 1.3×	37 1.1×	19	314
Moritz Klischat	71 0.4×	174 1.7×	64 0.8×	109 2.1×	36 1.0×	11	293
David Isele	131 0.8×	96 1.0×	73 0.9×	80 1.6×	15 0.4×	35	259
Ashkan Jasour	84 0.5×	91 0.9×	124 1.6×	110 2.2×	19 0.5×	29	311
Qiang Lu	62 0.4×	186 1.8×	102 1.3×	86 1.7×	19 0.5×	10	324
Onay Urfalıoǧlu	72 0.4×	89 0.9×	124 1.6×	33 0.6×	17 0.5×	16	239
Ritchie Lee	133 0.8×	136 1.3×	24 0.3×	88 1.7×	66 1.9×	21	337
Stefan Wender	118 0.7×	83 0.8×	84 1.1×	26 0.5×	19 0.5×	14	250
Sumbal Malik	70 0.4×	141 1.4×	54 0.7×	79 1.5×	21 0.6×	26	336

Countries citing papers authored by Mark van der Wilk

Since Specialization

Citations

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

Fields of papers citing papers by Mark van der Wilk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark van der Wilk. 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 Mark van der Wilk. The network helps show where Mark van der Wilk may publish in the future.

Co-authorship network of co-authors of Mark van der Wilk

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

All Works

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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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