Pierre Jacob

2.4k total citations
67 papers, 918 citations indexed

About

Pierre Jacob is a scholar working on Statistics and Probability, Artificial Intelligence and Applied Mathematics. According to data from OpenAlex, Pierre Jacob has authored 67 papers receiving a total of 918 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Statistics and Probability, 22 papers in Artificial Intelligence and 8 papers in Applied Mathematics. Recurrent topics in Pierre Jacob's work include Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Inference (13 papers) and Markov Chains and Monte Carlo Methods (12 papers). Pierre Jacob is often cited by papers focused on Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Inference (13 papers) and Markov Chains and Monte Carlo Methods (12 papers). Pierre Jacob collaborates with scholars based in France, United States and United Kingdom. Pierre Jacob's co-authors include Nicolás Chopin, Omiros Papaspiliopoulos, Christian P. Robert, Stéphane Girard, Lawrence M. Murray, Anthony Lee, Mathieu Gerber, Yves F. Atchadé, John R. O’Leary and Jeremy Heng and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Scientific Reports.

In The Last Decade

Pierre Jacob

64 papers receiving 889 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pierre Jacob France 17 352 345 74 64 63 67 918
Yves F. Atchadé United States 15 391 1.1× 461 1.3× 70 0.9× 67 1.0× 36 0.6× 42 1.0k
Jayanta Kumar Ghosh India 16 127 0.4× 226 0.7× 33 0.4× 42 0.7× 28 0.4× 63 828
Tamás F. Móri Hungary 13 132 0.4× 196 0.6× 68 0.9× 128 2.0× 30 0.5× 77 831
Nial Friel Ireland 21 560 1.6× 508 1.5× 24 0.3× 83 1.3× 16 0.3× 63 1.3k
Omiros Papaspiliopoulos United Kingdom 20 929 2.6× 794 2.3× 329 4.4× 110 1.7× 97 1.5× 40 1.8k
Natesh S. Pillai United States 17 515 1.5× 716 2.1× 97 1.3× 61 1.0× 44 0.7× 38 1.3k
Małgorzata Bogdan Poland 17 126 0.4× 297 0.9× 43 0.6× 76 1.2× 48 0.8× 67 1.2k
James P. Hobert United States 22 912 2.6× 1.5k 4.4× 56 0.8× 196 3.1× 35 0.6× 68 2.1k
Tobias Rydén Sweden 14 430 1.2× 268 0.8× 334 4.5× 103 1.6× 61 1.0× 26 1.1k
Dean Isaacson United States 13 223 0.6× 248 0.7× 58 0.8× 130 2.0× 84 1.3× 34 1.1k

Countries citing papers authored by Pierre Jacob

Since Specialization
Citations

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

Fields of papers citing papers by Pierre Jacob

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pierre Jacob

This figure shows the co-authorship network connecting the top 25 collaborators of Pierre Jacob. A scholar is included among the top collaborators of Pierre Jacob 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 Pierre Jacob. Pierre Jacob 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.
Cannet, Arnaud, Aymeric Histace, Mohammad Akhoundi, et al.. (2025). Application of wings interferential patterns (WIPs) and deep learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance. Scientific Reports. 15(1). 21548–21548. 1 indexed citations
2.
Cannet, Arnaud, Aymeric Histace, Mohammad Akhoundi, et al.. (2024). An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning. Scientific Data. 11(1). 4–4. 5 indexed citations
3.
Cannet, Arnaud, Mohammad Akhoundi, Aymeric Histace, et al.. (2023). Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species. Scientific Reports. 13(1). 13895–13895. 11 indexed citations
4.
Jacob, Pierre, et al.. (2023). VK-SITS: a Robust Time-Surface for Fast Event-Based Recognition. 1–6. 3 indexed citations
5.
Cannet, Arnaud, Aymeric Histace, Mohammad Akhoundi, et al.. (2023). Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest. Scientific Reports. 13(1). 17628–17628. 8 indexed citations
6.
Jacob, Pierre, et al.. (2023). VK-SITS: Variable Kernel Speed Invariant Time Surface for Event-Based Recognition. 754–761. 1 indexed citations
7.
Heng, Jeremy, et al.. (2022). An Invitation to Sequential Monte Carlo Samplers. Journal of the American Statistical Association. 117(539). 1587–1600. 25 indexed citations
8.
Cannet, Arnaud, Mohammad Akhoundi, Aymeric Histace, et al.. (2022). Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification. Scientific Reports. 12(1). 20086–20086. 9 indexed citations
9.
O’Leary, John R., et al.. (2021). Maximal Couplings of the Metropolis-Hastings Algorithm.. International Conference on Artificial Intelligence and Statistics. 1225–1233. 1 indexed citations
10.
Kishore, Nishant, Aimee R. Taylor, Pierre Jacob, et al.. (2021). Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study. The Lancet Digital Health. 4(1). e27–e36. 25 indexed citations
11.
Jacob, Pierre, et al.. (2020). Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency. Figshare. 13 indexed citations
12.
Taylor, Aimee R., Pierre Jacob, Daniel E. Neafsey, & Caroline O. Buckee. (2019). Estimating Relatedness Between Malaria Parasites. Genetics. 212(4). 1337–1351. 43 indexed citations
13.
Jacob, Pierre. (2015). Sequential Bayesian inference for implicit hidden Markov models and current limitations. SHILAP Revista de lepidopterología. 7 indexed citations
14.
Murray, Lawrence M., Anthony Lee, & Pierre Jacob. (2013). Rethinking resampling in the particle filter on graphics processing units. arXiv (Cornell University). 4 indexed citations
15.
Jacob, Pierre & Paulo Eduardo Oliveira. (2011). Local smoothing with given marginals. Journal of Statistical Computation and Simulation. 82(6). 915–926. 1 indexed citations
16.
Girard, Stéphane & Pierre Jacob. (2007). Frontier estimation via kernel regression on high power-transformed data. Journal of Multivariate Analysis. 99(3). 403–420. 13 indexed citations
17.
Jacob, Pierre. (2001). Identidad personal y aprendizaje. 20–23. 1 indexed citations
18.
Jacob, Pierre & Marc Jeannerod. (1999). QUAND VOIR, C'EST FAIRE. Revue internationale de philosophie. 53(209). 293–319. 2 indexed citations
19.
Jacob, Pierre, et al.. (1991). Stability of extreme value for a multidimensional sample. French digital mathematics library (Numdam). 16(1). 1–21. 4 indexed citations
20.
Jacob, Pierre. (1987). Un doublet dans la geographie livienne de l'Espagne antique: les Ausetans de l'Ebre. 135–148. 2 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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