Gabriel Haeser

86 total papers · 1.2k total citations
51 papers, 725 citations indexed

About

Gabriel Haeser is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Gabriel Haeser has authored 51 papers receiving a total of 725 indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Numerical Analysis, 45 papers in Computational Theory and Mathematics and 28 papers in Computational Mechanics. Recurrent topics in Gabriel Haeser's work include Advanced Optimization Algorithms Research (47 papers), Optimization and Variational Analysis (39 papers) and Sparse and Compressive Sensing Techniques (25 papers). Gabriel Haeser is often cited by papers focused on Advanced Optimization Algorithms Research (47 papers), Optimization and Variational Analysis (39 papers) and Sparse and Compressive Sensing Techniques (25 papers). Gabriel Haeser collaborates with scholars based in Brazil, Chile and United States. Gabriel Haeser's co-authors include Roberto Andreani, Paulo J. S. Silva, María Laura Schuverdt, J. M. Martı́nez, Alberto Ramos, Yinyu Ye, Ernesto G. Birgin, Hongcheng Liu, Vinícius Veloso de Melo and Oliver Hinder and has published in prestigious journals such as SHILAP Revista de lepidopterología, Fuzzy Sets and Systems and Mathematical Programming.

In The Last Decade

Gabriel Haeser

45 papers receiving 675 citations

Author Peers

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

Author						Papers	Cites
Gabriel Haeser	597	585	302	138	72	51	725
Levent Tunçel	526 0.9×	605 1.0×	161 0.5×	109 0.8×	91 1.3×	76	859
Masakazu Muramatsu	454 0.8×	413 0.7×	162 0.5×	156 1.1×	51 0.7×	36	691
L. M. Graña Drummond	660 1.1×	758 1.3×	143 0.5×	168 1.2×	119 1.7×	17	901
Luis M. Briceño-Arias	244 0.4×	338 0.6×	252 0.8×	71 0.5×	26 0.4×	34	633
Rafael Corrêa	437 0.7×	633 1.1×	163 0.5×	130 0.9×	27 0.4×	51	849
Jan-J. Rückmann	404 0.7×	460 0.8×	72 0.2×	206 1.5×	30 0.4×	33	684
B. N. Pshenichnyĭ	307 0.5×	417 0.7×	82 0.3×	159 1.2×	36 0.5×	69	882
Gerd Wachsmuth	175 0.3×	469 0.8×	280 0.9×	134 1.0×	21 0.3×	51	695
Soon‐Yi Wu	458 0.8×	604 1.0×	183 0.6×	140 1.0×	25 0.3×	69	841
Nguyen Mau Nam	510 0.9×	676 1.2×	117 0.4×	115 0.8×	33 0.5×	54	810

Countries citing papers authored by Gabriel Haeser

Since Specialization

Citations

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

Fields of papers citing papers by Gabriel Haeser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gabriel Haeser

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Haeser. A scholar is included among the top collaborators of Gabriel Haeser 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 Gabriel Haeser. Gabriel Haeser 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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