Massimo Vergassola

13.6k total citations · 5 hit papers
118 papers, 9.4k citations indexed

About

Massimo Vergassola is a scholar working on Molecular Biology, Computational Mechanics and Biomedical Engineering. According to data from OpenAlex, Massimo Vergassola has authored 118 papers receiving a total of 9.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 40 papers in Computational Mechanics and 17 papers in Biomedical Engineering. Recurrent topics in Massimo Vergassola's work include Fluid Dynamics and Turbulent Flows (38 papers), Neurobiology and Insect Physiology Research (10 papers) and Gene Regulatory Network Analysis (10 papers). Massimo Vergassola is often cited by papers focused on Fluid Dynamics and Turbulent Flows (38 papers), Neurobiology and Insect Physiology Research (10 papers) and Gene Regulatory Network Analysis (10 papers). Massimo Vergassola collaborates with scholars based in France, Italy and United States. Massimo Vergassola's co-authors include Roberto Benzi, Sauro Succi, Antonio Celani, Krzysztof Gawędzki, Gregory Falkovich, U. Frisch, Boris I. Shraiman, Emmanuel Villermaux, Andrea Mazzino and Eduardo P. C. Rocha and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Massimo Vergassola

117 papers receiving 9.1k citations

Hit Papers

The lattice Boltzmann equ... 1992 2026 2003 2014 1992 2001 2009 2007 2014 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Massimo Vergassola France 46 3.6k 2.6k 1.1k 819 760 118 9.4k
Jayanth R. Banavar United States 64 1.7k 0.5× 3.2k 1.2× 1.8k 1.7× 705 0.9× 826 1.1× 309 14.4k
Boris I. Shraiman United States 61 1.7k 0.5× 3.1k 1.2× 1.6k 1.5× 1.4k 1.7× 596 0.8× 120 16.6k
Lev S. Tsimring United States 56 1.5k 0.4× 5.5k 2.1× 2.2k 2.0× 1.5k 1.8× 478 0.6× 175 14.5k
Albert Libchaber United States 65 3.5k 1.0× 6.9k 2.7× 4.6k 4.2× 428 0.5× 1.1k 1.5× 178 18.9k
John Guckenheimer United States 52 1.8k 0.5× 1.3k 0.5× 1.6k 1.5× 1.2k 1.4× 830 1.1× 145 21.9k
Yuri A. Kuznetsov Russia 47 1.4k 0.4× 1.1k 0.4× 548 0.5× 2.1k 2.6× 812 1.1× 254 14.4k
Lee A. Segel Israel 44 1.1k 0.3× 4.4k 1.7× 1.7k 1.6× 1.4k 1.7× 393 0.5× 142 11.8k
Gerhard Wanner Germany 71 7.0k 1.9× 7.0k 2.7× 1.4k 1.3× 983 1.2× 295 0.4× 281 30.7k
A. Arnéodo France 58 769 0.2× 3.5k 1.4× 471 0.4× 489 0.6× 670 0.9× 228 10.7k
Stephen Wiggins United Kingdom 49 1.5k 0.4× 813 0.3× 994 0.9× 443 0.5× 523 0.7× 222 14.3k

Countries citing papers authored by Massimo Vergassola

Since Specialization
Citations

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

Fields of papers citing papers by Massimo Vergassola

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Massimo Vergassola

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Vergassola. A scholar is included among the top collaborators of Massimo Vergassola 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 Massimo Vergassola. Massimo Vergassola 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.
Hayden, Luke, Anna Chao, Noah Mitchell, et al.. (2025). Topological interactions drive the first fate decision in the Drosophila embryo. Nature Physics. 21(4). 632–643. 1 indexed citations
2.
Costa, Antonio Carlos, et al.. (2024). Fluctuating Landscapes and Heavy Tails in Animal Behavior. PubMed. 2(2). 8 indexed citations
3.
Broggini, Thomas, Xiang Ji, Rui Liu, et al.. (2024). Long-wavelength traveling waves of vasomotion modulate the perfusion of cortex. Neuron. 112(14). 2349–2367.e8. 16 indexed citations
4.
Biferale, Luca, et al.. (2023). Optimal policies for Bayesian olfactory search in turbulent flows. Physical review. E. 107(5). 55105–55105. 10 indexed citations
5.
Puliafito, Alberto, et al.. (2023). Two-fluid dynamics and micron-thin boundary layers shape cytoplasmic flows in earlyDrosophilaembryos. Proceedings of the National Academy of Sciences. 120(44). e2302879120–e2302879120. 8 indexed citations
6.
Vergassola, Massimo, et al.. (2022). Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints. Proceedings of the National Academy of Sciences. 119(8). 13 indexed citations
7.
Reddy, Gautam, et al.. (2022). Alternation emerges as a multi-modal strategy for turbulent odor navigation. eLife. 11. 9 indexed citations
8.
Katta, S., Alessandro Sanzeni, Alakananda Das, Massimo Vergassola, & Miriam B. Goodman. (2019). Progressive recruitment of distal MEC-4 channels determines touch response strength in C. elegans. The Journal of General Physiology. 151(10). 1213–1230. 8 indexed citations
9.
Reddy, Gautam, Joseph D. Zak, Massimo Vergassola, & Venkatesh N. Murthy. (2018). Antagonism in olfactory receptor neurons and its implications for the perception of odor mixtures. eLife. 7. 57 indexed citations
10.
Reddy, Gautam, Jérôme Wong-Ng, Antonio Celani, Terrence J. Sejnowski, & Massimo Vergassola. (2018). Glider soaring via reinforcement learning in the field. Nature. 562(7726). 236–239. 107 indexed citations
11.
Nadal, Céline, et al.. (2016). Statistical Distribution of Quantum Entanglement for a Random Bipartite State. 25 indexed citations
12.
Wong-Ng, Jérôme, Anna Melbinger, Antonio Celani, & Massimo Vergassola. (2016). The Role of Adaptation in Bacterial Speed Races. PLoS Computational Biology. 12(6). e1004974–e1004974. 21 indexed citations
13.
Voisinne, Guillaume, Briana G. Nixon, Anna Melbinger, et al.. (2015). T Cells Integrate Local and Global Cues to Discriminate between Structurally Similar Antigens. Cell Reports. 11(8). 1208–1219. 45 indexed citations
14.
Taheri-Araghi, Sattar, Serena Bradde, John T. Sauls, et al.. (2014). Cell-Size Control and Homeostasis in Bacteria. Current Biology. 25(3). 385–391. 463 indexed citations breakdown →
15.
Celani, Antonio & Massimo Vergassola. (2012). Nonlinearity, Fluctuations, and Response in Sensory Systems. Physical Review Letters. 108(25). 258102–258102. 9 indexed citations
16.
Bailly‐Bechet, Marc, Massimo Vergassola, & Eduardo P. C. Rocha. (2007). Causes for the intriguing presence of tRNAs in phages. Genome Research. 17(10). 1486–1495. 300 indexed citations
17.
Fischer, Gilles, Eduardo P. C. Rocha, Frédéric Brunet, Massimo Vergassola, & Bernard Dujon. (2006). Highly Variable Rates of Genome Rearrangements betweenHemiascomycetous Yeast Lineages. PLoS Genetics. 2(3). e32–e32. 72 indexed citations
18.
Boffetta, G., Antonio Celani, Andrea Mazzino, Alberto Puliafito, & Massimo Vergassola. (2005). The viscoelastic Kolmogorov flow: eddy viscosity and linear stability. Journal of Fluid Mechanics. 523. 161–170. 34 indexed citations
19.
Castiglione, Patrizia, A. Crisanti, Andrea Mazzino, Massimo Vergassola, & Angelo Vulpiani. (1998). Resonant enhanced diffusion in time-dependent flow. Journal of Physics A Mathematical and General. 31(35). 7197–7210. 22 indexed citations
20.
Vergassola, Massimo & U. Frisch. (1991). Wavelet transforms of self-similar processes. Physica D Nonlinear Phenomena. 54(1-2). 58–64. 37 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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