Matthew San Jose

12 total papers · 635 total citations
9 papers, 237 citations indexed

About

Matthew San Jose is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Hardware and Architecture. According to data from OpenAlex, Matthew San Jose has authored 9 papers receiving a total of 237 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Electrical and Electronic Engineering, 2 papers in Materials Chemistry and 1 paper in Hardware and Architecture. Recurrent topics in Matthew San Jose's work include Ferroelectric and Negative Capacitance Devices (9 papers), Advanced Memory and Neural Computing (7 papers) and Semiconductor materials and devices (5 papers). Matthew San Jose is often cited by papers focused on Ferroelectric and Negative Capacitance Devices (9 papers), Advanced Memory and Neural Computing (7 papers) and Semiconductor materials and devices (5 papers). Matthew San Jose collaborates with scholars based in United States and India. Matthew San Jose's co-authors include Suman Datta, Huacheng Ye, Shimeng Yu, Sourav Dutta, Abhishek Khanna, Wriddhi Chakraborty, Benjamin Grisafe, Yuan-Chun Luo, Ian V. Lightcap and Subhash L. Shindé and has published in prestigious journals such as IEEE Transactions on Electron Devices, 2021 IEEE International Electron Devices Meeting (IEDM) and 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits).

In The Last Decade

Matthew San Jose

9 papers receiving 234 citations

Author Peers

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

Author						Papers	Cites
Matthew San Jose	228	58	21	16	15	9	237
E. Vecchio	228 1.0×	58 1.0×	9 0.4×	22 1.4×	6 0.4×	16	243
Nellie Laleni	274 1.2×	61 1.1×	34 1.6×	14 0.9×	23 1.5×	18	300
Chao Li	248 1.1×	97 1.7×	21 1.0×	39 2.4×	36 2.4×	17	280
Gicheol Shin	268 1.2×	71 1.2×	13 0.6×	42 2.6×	47 3.1×	17	298
O. Golonzka	177 0.8×	16 0.3×	7 0.3×	19 1.2×	12 0.8×	6	182
Hyuntaek Jung	209 0.9×	16 0.3×	14 0.7×	9 0.6×	28 1.9×	13	243
Jingjie Niu	226 1.0×	155 2.7×	28 1.3×	37 2.3×	4 0.3×	11	323
Dong Ik Suh	251 1.1×	124 2.1×	22 1.0×	52 3.3×	2 0.1×	17	297
G. Cibrario	220 1.0×	9 0.2×	13 0.6×	13 0.8×	30 2.0×	18	228
T. Marangon	144 0.6×	124 2.1×	4 0.2×	9 0.6×	13 0.9×	14	177

Countries citing papers authored by Matthew San Jose

Since Specialization

Citations

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

Fields of papers citing papers by Matthew San Jose

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew San Jose

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew San Jose. A scholar is included among the top collaborators of Matthew San Jose 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 San Jose. Matthew San Jose 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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