Wannes Meert

2.4k total citations
70 papers, 1.3k citations indexed

About

Wannes Meert is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Wannes Meert has authored 70 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 18 papers in Computer Networks and Communications and 14 papers in Signal Processing. Recurrent topics in Wannes Meert's work include Bayesian Modeling and Causal Inference (24 papers), Anomaly Detection Techniques and Applications (11 papers) and Logic, Reasoning, and Knowledge (10 papers). Wannes Meert is often cited by papers focused on Bayesian Modeling and Causal Inference (24 papers), Anomaly Detection Techniques and Applications (11 papers) and Logic, Reasoning, and Knowledge (10 papers). Wannes Meert collaborates with scholars based in Belgium, United States and South Africa. Wannes Meert's co-authors include Sofie Pollin, Brecht Reynders, Jesse Davis, Marian Verhelst, Guy Van den Broeck, Steven Lauwereins, Luc De Raedt, Vincent Lenders, Sreeraj Rajendran and Nima Taghipour and has published in prestigious journals such as PLoS ONE, IEEE Access and IEEE Journal of Solid-State Circuits.

In The Last Decade

Wannes Meert

63 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wannes Meert Belgium 19 533 456 413 232 190 70 1.3k
Luis Antonio Ribot García United States 16 282 0.5× 211 0.5× 232 0.6× 132 0.6× 196 1.0× 89 1.0k
Ahmad Akbari Iran 18 359 0.7× 164 0.4× 264 0.6× 74 0.3× 320 1.7× 140 1.0k
Maurizio Mongelli Italy 16 441 0.8× 285 0.6× 646 1.6× 81 0.3× 216 1.1× 98 1.1k
João M. P. Cardoso Portugal 20 468 0.9× 281 0.6× 722 1.7× 157 0.7× 127 0.7× 157 1.8k
Chieh-Jan Mike Liang United States 17 117 0.2× 676 1.5× 1.1k 2.6× 130 0.6× 127 0.7× 42 1.5k
Graziano Pravadelli Italy 20 231 0.4× 396 0.9× 266 0.6× 164 0.7× 74 0.4× 159 1.5k
Pedro C. Diniz United States 19 338 0.6× 332 0.7× 798 1.9× 124 0.5× 113 0.6× 97 1.7k
Yung‐Fa Huang Taiwan 18 133 0.2× 612 1.3× 691 1.7× 126 0.5× 52 0.3× 183 1.2k
Sana Ullah Jan United Kingdom 16 512 1.0× 179 0.4× 562 1.4× 88 0.4× 271 1.4× 45 1.4k
Mohamed Zohdy United States 15 369 0.7× 358 0.8× 404 1.0× 38 0.2× 180 0.9× 175 1.2k

Countries citing papers authored by Wannes Meert

Since Specialization
Citations

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

Fields of papers citing papers by Wannes Meert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wannes Meert

This figure shows the co-authorship network connecting the top 25 collaborators of Wannes Meert. A scholar is included among the top collaborators of Wannes Meert 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 Wannes Meert. Wannes Meert 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
1.
Davis, Jesse, et al.. (2024). Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Machine Learning. 113(9). 6977–7010. 19 indexed citations
2.
D’hulst, Reinhilde, et al.. (2023). Scenario generation of residential electricity consumption through sampling of historical data. Sustainable Energy Grids and Networks. 34. 100985–100985. 7 indexed citations
3.
Meert, Wannes, et al.. (2023). Efficient Execution of Irregular Dataflow Graphs. Lirias (KU Leuven). 1 indexed citations
4.
Dey, Bappaditya, et al.. (2022). Code Generation Using Machine Learning: A Systematic Review. IEEE Access. 10. 82434–82455. 31 indexed citations
5.
Schütte, Kurt, et al.. (2018). Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running. PLoS ONE. 13(6). e0199509–e0199509. 21 indexed citations
6.
Meert, Wannes, et al.. (2018). Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. Lirias (KU Leuven). 606–615. 38 indexed citations
7.
Reynders, Brecht, Wannes Meert, & Sofie Pollin. (2017). Power and spreading factor control in low power wide area networks. Lirias (KU Leuven). 1–6. 160 indexed citations
8.
Kimmig, Angelika, et al.. (2016). Knowledge compilation and weighted model counting for inference in probabilistic logic programs. Lirias (KU Leuven). 359–364. 1 indexed citations
9.
Reynders, Brecht, Wannes Meert, & Sofie Pollin. (2016). Range and coexistence analysis of long range unlicensed communication. Lirias (KU Leuven). 1–6. 164 indexed citations
10.
Broeck, Guy Van den, et al.. (2015). Anytime inference in probabilistic logic programs with TP-compilation. Lirias (KU Leuven). 1852–1858. 14 indexed citations
11.
Lauwereins, Steven, et al.. (2015). 24.2 Context-aware hierarchical information-sensing in a 6μW 90nm CMOS voice activity detector. Lirias (KU Leuven). 1–3. 30 indexed citations
12.
Haaren, Jan Van, Guy Van den Broeck, Wannes Meert, & Jesse Davis. (2014). Tractable learning of liftable Markov logic networks. Lirias (KU Leuven). 1–9. 3 indexed citations
13.
Meert, Wannes, et al.. (2014). Efficient probabilistic inference for dynamic relational models. Lirias (KU Leuven). 131–132. 1 indexed citations
14.
Lauwereins, Steven, et al.. (2014). Context- and cost-aware feature selection in ultra-low-power sensor interfaces. Lirias (KU Leuven). 93–98. 7 indexed citations
15.
Broeck, Guy Van den, Wannes Meert, & Jesse Davis. (2013). Lifted generative parameter learning. Lirias (KU Leuven). 87–94. 5 indexed citations
16.
Meert, Wannes, Guy Van den Broeck, Nima Taghipour, et al.. (2012). Lifted inference for probabilistic programming. Lirias (KU Leuven). 2 indexed citations
17.
Broeck, Guy Van den, Nima Taghipour, Wannes Meert, Jesse Davis, & Luc De Raedt. (2011). Lifted probabilistic inference by first-order knowledge compilation. Lirias (KU Leuven). 87 indexed citations
18.
Meert, Wannes, Jan Struyf, & Hendrik Blockeel. (2010). Contextual variable elimination with overlapping contexts. Lirias (KU Leuven). 193–201.
19.
Meert, Wannes, Jan Struyf, & Hendrik Blockeel. (2008). Learning Ground CP-logic Theories by means of Bayesian Network Techniques. Lirias (KU Leuven). 5 indexed citations
20.
Meert, Wannes, Jan Struyf, & Hendrik Blockeel. (2008). Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques. Fundamenta Informaticae. 89(1). 131–160. 21 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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