Maxime Jacquot

4.7k total citations
75 papers, 3.3k citations indexed

About

Maxime Jacquot is a scholar working on Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering and Biomedical Engineering. According to data from OpenAlex, Maxime Jacquot has authored 75 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Atomic and Molecular Physics, and Optics, 28 papers in Electrical and Electronic Engineering and 22 papers in Biomedical Engineering. Recurrent topics in Maxime Jacquot's work include Neural Networks and Reservoir Computing (19 papers), Photonic and Optical Devices (17 papers) and Advanced Fiber Laser Technologies (15 papers). Maxime Jacquot is often cited by papers focused on Neural Networks and Reservoir Computing (19 papers), Photonic and Optical Devices (17 papers) and Advanced Fiber Laser Technologies (15 papers). Maxime Jacquot collaborates with scholars based in France, Belgium and Spain. Maxime Jacquot's co-authors include Laurent Larger, John M. Dudley, François Courvoisier, Luca Furfaro, Yanne K. Chembo, P.-A. Lacourt, Daniel Brunner, Luc Froehly, M. K. Bhuyan and Romain Martinenghi and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Reviews of Modern Physics.

In The Last Decade

Maxime Jacquot

72 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxime Jacquot France 29 1.6k 1.3k 1.2k 894 531 75 3.3k
Junji Ohtsubo Japan 24 1.1k 0.7× 521 0.4× 298 0.3× 213 0.2× 233 0.4× 117 2.2k
Morten Bache Denmark 25 1.1k 0.7× 2.3k 1.7× 627 0.5× 335 0.4× 52 0.1× 82 3.7k
Yaron Bromberg Israel 27 997 0.6× 2.3k 1.7× 1.5k 1.3× 722 0.8× 179 0.3× 77 4.3k
Murti V. Salapaka United States 25 903 0.6× 1.5k 1.1× 289 0.3× 501 0.6× 64 0.1× 182 3.0k
A. Gatti Italy 32 641 0.4× 3.3k 2.4× 1.5k 1.3× 490 0.5× 100 0.2× 108 5.0k
Dana Z. Anderson United States 35 1.3k 0.8× 3.3k 2.4× 978 0.8× 185 0.2× 54 0.1× 129 4.5k
Hui Cao United States 41 2.9k 1.8× 5.8k 4.3× 1.1k 1.0× 1.6k 1.7× 134 0.3× 183 9.1k
Raymond G. Beausoleil United States 46 5.5k 3.5× 4.5k 3.3× 1.5k 1.3× 830 0.9× 191 0.4× 324 8.4k
Ling-An Wu China 30 946 0.6× 3.7k 2.7× 2.0k 1.7× 412 0.5× 119 0.2× 146 5.3k
Leslie A. Rusch Canada 38 5.2k 3.3× 2.2k 1.6× 644 0.6× 1.2k 1.4× 111 0.2× 385 6.2k

Countries citing papers authored by Maxime Jacquot

Since Specialization
Citations

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

Fields of papers citing papers by Maxime Jacquot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxime Jacquot

This figure shows the co-authorship network connecting the top 25 collaborators of Maxime Jacquot. A scholar is included among the top collaborators of Maxime Jacquot 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 Maxime Jacquot. Maxime Jacquot 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.
Froehly, Luc, et al.. (2024). Physics-driven learning for digital holographic microscopy. SHILAP Revista de lepidopterología. 309. 15005–15005.
2.
Sandoz, Patrick, et al.. (2024). Digital holographic microscopy applied to 3D computer micro-vision by using deep neural networks. Journal of the European Optical Society Rapid Publications. 20(2). 31–31. 1 indexed citations
3.
Sandoz, Patrick, et al.. (2023). Digital holographic microscopy applied to 3D computer microvision by using deep neural networks. SHILAP Revista de lepidopterología. 287. 13011–13011. 1 indexed citations
4.
Jacquot, Maxime, et al.. (2023). I learned it through the hologram. HAL (Le Centre pour la Communication Scientifique Directe). 31–35. 1 indexed citations
5.
Sandoz, Patrick, et al.. (2022). Pose Measurement at Small Scale by Spectral Analysis of Periodic Patterns. International Journal of Computer Vision. 130(6). 1566–1582. 8 indexed citations
6.
Moughames, Johnny, Xavier Porté, Laurent Larger, et al.. (2020). 3D printed multimode-splitters for photonic interconnects. Optical Materials Express. 10(11). 2952–2952. 39 indexed citations
7.
Sandoz, Patrick, et al.. (2020). Robust Phase-Based Decoding for Absolute (X, Y, Θ) Positioning by Vision. IEEE Transactions on Instrumentation and Measurement. 70. 1–12. 13 indexed citations
8.
Moughames, Johnny, Xavier Porté, Michael Thiel, et al.. (2020). Three-dimensional waveguide interconnects for scalable integration of photonic neural networks. Optica. 7(6). 640–640. 85 indexed citations
9.
Sandoz, Patrick, et al.. (2020). Sensing One Nanometer Over Ten Centimeters: A Microencoded Target for Visual In-Plane Position Measurement. IEEE/ASME Transactions on Mechatronics. 25(3). 1193–1201. 43 indexed citations
10.
Froehly, Luc, et al.. (2019). Diffractive Coupling For Photonic Networks: How Big Can We Go?. IEEE Journal of Selected Topics in Quantum Electronics. 26(1). 1–8. 17 indexed citations
11.
Froehly, Luc, François Courvoisier, Daniel Brunner, et al.. (2019). Advancing Fourier: space–time concepts in ultrafast optics, imaging, and photonic neural networks. Journal of the Optical Society of America A. 36(11). C69–C69. 4 indexed citations
12.
Brunner, Daniel, Bogdan Penkovsky, Bicky A. Márquez, et al.. (2018). Tutorial: Photonic neural networks in delay systems. Journal of Applied Physics. 124(15). 105 indexed citations
13.
Bueno, J. Trujillo, Luc Froehly, Ingo Fischer, et al.. (2018). Reinforcement learning in a large-scale photonic recurrent neural network. Optica. 5(6). 756–756. 267 indexed citations
14.
Jacquot, Maxime, et al.. (2018). Digital Holography as Computer Vision Position Sensor with an Extended Range of Working Distances. Sensors. 18(7). 2005–2005. 5 indexed citations
15.
Lin, Guoping, Rémi Henriet, Aurélien Coillet, et al.. (2018). Dependence of quality factor on surface roughness in crystalline whispering-gallery mode resonators. Optics Letters. 43(3). 495–495. 28 indexed citations
16.
Meyer, Rémi, Remo Giust, Maxime Jacquot, John M. Dudley, & François Courvoisier. (2017). Submicron-quality cleaving of glass with elliptical ultrafast Bessel beams. Applied Physics Letters. 111(23). 27 indexed citations
17.
Courvoisier, François, A. Mathis, Luc Froehly, et al.. (2012). Sending femtosecond pulses in circles: highly nonparaxial accelerating beams. Optics Letters. 37(10). 1736–1736. 76 indexed citations
18.
Erneux, Thomas, et al.. (2012). Crenelated fast oscillatory outputs of a two-delay electro-optic oscillator. Physical Review E. 85(2). 26206–26206. 22 indexed citations
19.
Courvoisier, François, P.-A. Lacourt, Maxime Jacquot, et al.. (2009). Surface nanoprocessing with nondiffracting femtosecond Bessel beams. Optics Letters. 34(20). 3163–3163. 78 indexed citations
20.
Jacquot, Maxime, et al.. (1966). Le Cheval : encyclopédie de l'équitation et des sports hippiques.. Larousse eBooks. 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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