Émile Richard

643 total citations
12 papers, 272 citations indexed

About

Émile Richard is a scholar working on Computational Mechanics, Signal Processing and Strategy and Management. According to data from OpenAlex, Émile Richard has authored 12 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Mechanics, 4 papers in Signal Processing and 2 papers in Strategy and Management. Recurrent topics in Émile Richard's work include Sparse and Compressive Sensing Techniques (7 papers), Blind Source Separation Techniques (4 papers) and ECG Monitoring and Analysis (2 papers). Émile Richard is often cited by papers focused on Sparse and Compressive Sensing Techniques (7 papers), Blind Source Separation Techniques (4 papers) and ECG Monitoring and Analysis (2 papers). Émile Richard collaborates with scholars based in United States, France and Canada. Émile Richard's co-authors include Andrea Montanari, Nicolas Vayatis, Pierre-André Savalle, Adrian D. C. Chan, Theodoros Evgeniou, Kaifu Zhang, Vineet Padmanabhan, Georges Goetz, Stéphane Gaïffas and E. J. Chichilnisky and has published in prestigious journals such as IEEE Transactions on Information Theory, Marketing Science and Journal of Machine Learning Research.

In The Last Decade

Émile Richard

12 papers receiving 267 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Émile Richard United States	8	99	55	44	36	33	12	272
Dan Kushnir United States	10	63 0.6×	102 1.9×	63 1.4×	6 0.2×	7 0.2×	15	311
Yunzhang Zhu United States	10	117 1.2×	174 3.2×	29 0.7×	220 6.1×	16 0.5×	19	492
Chris K. Williams United States	5	40 0.4×	133 2.4×	36 0.8×	19 0.5×	6 0.2×	8	285
Will Wei Sun United States	11	74 0.7×	95 1.7×	20 0.5×	28 0.8×	134 4.1×	23	292
Ambedkar Dukkipati India	10	23 0.2×	136 2.5×	9 0.2×	26 0.7×	40 1.2×	43	337
Kaizheng Wang United States	4	98 1.0×	60 1.1×	40 0.9×	83 2.3×	9 0.3×	7	255
Kai-Yang Chiang United States	8	84 0.8×	298 5.4×	51 1.2×	8 0.2×	21 0.6×	9	465
Hao Ji United States	9	44 0.4×	68 1.2×	20 0.5×	9 0.3×	3 0.1×	31	251
N. Nefedov Finland	8	17 0.2×	154 2.8×	19 0.4×	4 0.1×	15 0.5×	32	378

Countries citing papers authored by Émile Richard

Since Specialization

Citations

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

Fields of papers citing papers by Émile Richard

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Émile Richard

This figure shows the co-authorship network connecting the top 25 collaborators of Émile Richard. A scholar is included among the top collaborators of Émile Richard 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 Émile Richard. Émile Richard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

Richard, Émile, Georges Goetz, & E. J. Chichilnisky. (2015). Recognizing retinal ganglion cells in the dark. neural information processing systems. 28. 2476–2484. 14 indexed citations

Montanari, Andrea & Émile Richard. (2015). Non-Negative Principal Component Analysis: Message Passing Algorithms and Sharp Asymptotics. IEEE Transactions on Information Theory. 62(3). 1458–1484. 51 indexed citations

Richard, Émile & Andrea Montanari. (2014). A statistical model for tensor PCA. Neural Information Processing Systems. 27. 2897–2905. 32 indexed citations

Richard, Émile & Guillaume Obozinski. (2014). Tight convex relaxations for sparse matrix factorization. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

Deshpande, Yash, Andrea Montanari, & Émile Richard. (2014). Cone-Constrained Principal Component Analysis. Neural Information Processing Systems. 27. 2717–2725. 12 indexed citations

Richard, Émile, et al.. (2013). Intersecting singularities for multi-structured estimation. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

Richard, Émile, Stéphane Gaïffas, & Nicolas Vayatis. (2012). Link Prediction in Graphs with Autoregressive Features. Journal of Machine Learning Research. 15(1). 565–593. 15 indexed citations

Richard, Émile, Pierre-André Savalle, & Nicolas Vayatis. (2012). Estimation of Simultaneously Sparse and Low Rank Matrices. arXiv (Cornell University). 51–58. 73 indexed citations

Richard, Émile & Adrian D. C. Chan. (2012). Non-obtrusive electrocardiogram system for the Smart Rollator. 1–5. 2 indexed citations

10.

Zhang, Kaifu, et al.. (2011). Content Contributor Management and Network Effects in a UGC Environment. SSRN Electronic Journal. 2 indexed citations

11.

Zhang, Kaifu, Theodoros Evgeniou, Vineet Padmanabhan, & Émile Richard. (2011). Content Contributor Management and Network Effects in a UGC Environment. Marketing Science. 31(3). 433–447. 44 indexed citations

12.

Richard, Émile & Adrian D. C. Chan. (2010). Design of a gel-less two-electrode ECG monitor. 11. 92–96. 25 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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