Matthew Smith

1.7k total citations
9 papers, 153 citations indexed

About

Matthew Smith is a scholar working on Artificial Intelligence, Economics and Econometrics and Modeling and Simulation. According to data from OpenAlex, Matthew Smith has authored 9 papers receiving a total of 153 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Economics and Econometrics and 2 papers in Modeling and Simulation. Recurrent topics in Matthew Smith's work include COVID-19 epidemiological studies (2 papers), Air Quality and Health Impacts (1 paper) and Speech and Audio Processing (1 paper). Matthew Smith is often cited by papers focused on COVID-19 epidemiological studies (2 papers), Air Quality and Health Impacts (1 paper) and Speech and Audio Processing (1 paper). Matthew Smith collaborates with scholars based in Spain, Germany and Hungary. Matthew Smith's co-authors include Francisco Álvarez, Alfonso Valencia, Miguel Ponce-de-León, Nikita Moshkov, Mehrtash Babadi, Claire McQuin, Péter Horváth, Rebecca A. Senft, Yu Han and Shantanu Singh and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Scientific Reports.

In The Last Decade

Matthew Smith

8 papers receiving 147 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Matthew Smith Spain	6	40	26	22	16	15	9	153
David Pastor-Escuredo Spain	9	29 0.7×	11 0.4×	1 0.0×	17 1.1×	2 0.1×	17	199
Miguel A. Cabra de Luna South Korea	7	60 1.5×	5 0.2×	5 0.2×	3 0.2×		39	205
Rajmonda S. Caceres United States	8	32 0.8×	3 0.1×		55 3.4×	14 0.9×	16	183
Vivek Navale United States	3	40 1.0×	10 0.4×		53 3.3×		6	250
Shaukat Ali Pakistan	11	18 0.5×	1 0.0×	7 0.3×	60 3.8×	40 2.7×	28	226
Janosch Ortmann Canada	8	10 0.3×	3 0.1×		17 1.1×	2 0.1×	13	156
Luís Antunes Portugal	7	24 0.6×		5 0.2×	36 2.3×	3 0.2×	30	166
Rahmad Kurniawan Indonesia	8	83 2.1×	1 0.0×	21 1.0×	2 0.1×	8 0.5×	77	242
Casper F. Winsnes Sweden	4	25 0.6×	69 2.7×		63 3.9×		5	153
Mustapha Mokrane United Kingdom	6	41 1.0×	5 0.2×		43 2.7×		18	291

Countries citing papers authored by Matthew Smith

Since Specialization

Citations

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

Fields of papers citing papers by Matthew Smith

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Smith

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

All Works

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9 of 9 papers shown

1.

Moshkov, Nikita, Matthew Smith, Claire McQuin, et al.. (2024). Learning representations for image-based profiling of perturbations. Nature Communications. 15(1). 1594–1594. 35 indexed citations

2.

Smith, Matthew, Miguel Ponce-de-León, & Alfonso Valencia. (2022). Evaluating the policy of closing bars and restaurants in Cataluña and its effects on mobility and COVID19 incidence. Scientific Reports. 12(1). 9132–9132. 6 indexed citations

3.

Ponce-de-León, Miguel, José M. Fernández, Davide Cirillo, et al.. (2021). COVID-19 Flow-Maps an open geographic information system on COVID-19 and human mobility for Spain. Scientific Data. 8(1). 310–310. 17 indexed citations

4.

Smith, Matthew & Francisco Álvarez. (2021). Identifying mortality factors from Machine Learning using Shapley values – a case of COVID19. Expert Systems with Applications. 176. 114832–114832. 56 indexed citations

5.

Álvarez, Francisco & Matthew Smith. (2021). Using Shapley values to assess the impact of temporary traffic restrictions on NO2 levels in Madrid urban area. International Journal of Environmental Science and Technology. 18(11). 3343–3356. 4 indexed citations

6.

Smith, Matthew & Francisco Álvarez. (2021). A machine learning research template for binary classification problems and shapley values integration. Software Impacts. 8. 100074–100074. 7 indexed citations

7.

Smith, Matthew & Francisco Álvarez. (2021). Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting. Computational Economics. 59(1). 263–295. 27 indexed citations

8.

Stadelmann, Thilo, et al.. (2010). Rethinking Algorithm Design and Development in Speech Processing. 4476–4479. 1 indexed citations

9.

LoConte, Noelle K., James F. Cleary, Greg Wilding, et al.. (2008). Predictors of dose limiting toxicities in phase I clinical trials: The impact of age, comorbidity, and other clinical and non-clinical factors. Journal of Clinical Oncology. 26(15_suppl). 9525–9525.

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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