Elias S. Helou

520 total citations
29 papers, 301 citations indexed

About

Elias S. Helou is a scholar working on Computational Mechanics, Radiology, Nuclear Medicine and Imaging and Numerical Analysis. According to data from OpenAlex, Elias S. Helou has authored 29 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Mechanics, 13 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Numerical Analysis. Recurrent topics in Elias S. Helou's work include Sparse and Compressive Sensing Techniques (15 papers), Medical Imaging Techniques and Applications (10 papers) and Advanced Optimization Algorithms Research (9 papers). Elias S. Helou is often cited by papers focused on Sparse and Compressive Sensing Techniques (15 papers), Medical Imaging Techniques and Applications (10 papers) and Advanced Optimization Algorithms Research (9 papers). Elias S. Helou collaborates with scholars based in Brazil, United States and Moldova. Elias S. Helou's co-authors include Alvaro R. De Pierro, Marcelo V. W. Zibetti, Eduardo X. Miqueles, Luís Gustavo Nonato, Daniel Rodrigues Pipa, Sandra A. Santos, Ravinder R. Regatte, Paulo Pagliosa, Maria Cristina Ferreira de Oliveira and Gábor T. Herman and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Image Processing and Mathematical Programming.

In The Last Decade

Elias S. Helou

27 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Elias S. Helou Brazil 10 114 68 67 61 59 29 301
Caixia Kou China 8 137 1.2× 112 1.6× 130 1.9× 243 4.0× 116 2.0× 11 472
Gaetano Zanghirati Italy 8 169 1.5× 162 2.4× 44 0.7× 123 2.0× 71 1.2× 21 474
Dennis Amelunxen Germany 6 154 1.4× 33 0.5× 16 0.2× 26 0.4× 28 0.5× 9 232
Qiuwei Li United States 11 288 2.5× 113 1.7× 19 0.3× 71 1.2× 48 0.8× 47 461
Charles Dossal France 9 114 1.0× 36 0.5× 13 0.2× 41 0.7× 60 1.0× 18 234
Mert Pilancı United States 9 188 1.6× 59 0.9× 14 0.2× 49 0.8× 48 0.8× 47 368
Yohann de Castro France 8 220 1.9× 89 1.3× 46 0.7× 17 0.3× 25 0.4× 24 347
Yuyuan Ouyang United States 5 243 2.1× 39 0.6× 25 0.4× 134 2.2× 104 1.8× 17 348
Luca Nenna France 4 53 0.5× 65 1.0× 26 0.4× 13 0.2× 45 0.8× 10 364

Countries citing papers authored by Elias S. Helou

Since Specialization
Citations

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

Fields of papers citing papers by Elias S. Helou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elias S. Helou. 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 Elias S. Helou. The network helps show where Elias S. Helou may publish in the future.

Co-authorship network of co-authors of Elias S. Helou

This figure shows the co-authorship network connecting the top 25 collaborators of Elias S. Helou. A scholar is included among the top collaborators of Elias S. Helou 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 Elias S. Helou. Elias S. Helou 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.
Helou, Elias S., et al.. (2022). A primal nonsmooth reformulation for bilevel optimization problems. Mathematical Programming. 198(2). 1381–1409.
2.
Fendrich, Arthur Nicolaus, et al.. (2022). A scalable method for the estimation of spatial disaggregation models. Computers & Geosciences. 166. 105161–105161. 2 indexed citations
3.
Costa, Eduardo F., et al.. (2021). A Markovian Incremental Stochastic Subgradient Algorithm. IEEE Transactions on Automatic Control. 68(1). 124–139. 1 indexed citations
4.
Helou, Elias S.. (2020). Fast proximal gradient methods for nonsmooth convex optimization for tomographic image reconstruction. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 2 indexed citations
5.
Zibetti, Marcelo V. W., Elias S. Helou, Azadeh Sharafi, & Ravinder R. Regatte. (2020). Fast multicomponent 3D‐T1ρ relaxometry. NMR in Biomedicine. 33(12). e4318–e4318. 6 indexed citations
6.
Helou, Elias S., Marcelo V. W. Zibetti, Leon Axel, et al.. (2019). The discrete Fourier transform for golden angle linogram sampling. Inverse Problems. 35(12). 125004–125004. 1 indexed citations
7.
Zibetti, Marcelo V. W., Elias S. Helou, Ravinder R. Regatte, & Gábor T. Herman. (2018). Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging. IEEE Transactions on Computational Imaging. 5(1). 109–119. 26 indexed citations
8.
Helou, Elias S., et al.. (2017). Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction. Journal of Synchrotron Radiation. 25(1). 248–256. 1 indexed citations
9.
Helou, Elias S., et al.. (2017). ϵ-subgradient algorithms for bilevel convex optimization. Inverse Problems. 33(5). 55020–55020. 10 indexed citations
10.
Helou, Elias S., et al.. (2017). On the Local Convergence Analysis of the Gradient Sampling Method for Finite Max-Functions. Journal of Optimization Theory and Applications. 175(1). 137–157. 5 indexed citations
11.
Helou, Elias S., et al.. (2016). String-averaging incremental subgradients for constrained convex optimization with applications to reconstruction of tomographic images. Inverse Problems. 32(11). 115014–115014. 5 indexed citations
12.
Ponti, Moacir Antonelli, Elias S. Helou, Paulo J. S. G. Ferreira, & Nelson D. A. Mascarenhas. (2015). Image Restoration Using Gradient Iteration and Constraints for Band Extrapolation. IEEE Journal of Selected Topics in Signal Processing. 10(1). 71–80. 5 indexed citations
13.
Helou, Elias S., Yair Censor, Tai‐Been Chen, et al.. (2014). String-averaging expectation-maximization for maximum likelihood estimation in emission tomography. Inverse Problems. 30(5). 55003–55003. 6 indexed citations
14.
Petronetto, Fabiano, Afonso Paiva, Elias S. Helou, David E. Stewart, & Luís Gustavo Nonato. (2013). Mesh‐Free Discrete Laplace–Beltrami Operator. Computer Graphics Forum. 32(6). 214–226. 10 indexed citations
15.
Pagliosa, Paulo, et al.. (2013). Similarity Preserving Snippet-Based Visualization of Web Search Results. IEEE Transactions on Visualization and Computer Graphics. 20(3). 457–470. 51 indexed citations
16.
Helou, Elias S. & Alvaro R. De Pierro. (2010). On perturbed steepest descent methods with inexact line search for bilevel convex optimization. Optimization. 60(8-9). 991–1008. 8 indexed citations
17.
Helou, Elias S. & Alvaro R. De Pierro. (2009). Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods. SIAM Journal on Optimization. 20(3). 1547–1572. 38 indexed citations
18.
Pierro, Alvaro R. De & Elias S. Helou. (2009). From convex feasibility to convex constrained optimization using block action projection methods and underrelaxation. International Transactions in Operational Research. 16(4). 495–504. 6 indexed citations
19.
20.
Helou, Elias S. & Alvaro R. De Pierro. (2005). Convergence results for scaled gradient algorithms in positron emission tomography. Inverse Problems. 21(6). 1905–1914. 16 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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