Sijie Ma

929 total citations · 2 hit papers
12 papers, 654 citations indexed

About

Sijie Ma is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Sijie Ma has authored 12 papers receiving a total of 654 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 4 papers in Cellular and Molecular Neuroscience and 4 papers in Artificial Intelligence. Recurrent topics in Sijie Ma's work include Advanced Memory and Neural Computing (9 papers), CCD and CMOS Imaging Sensors (4 papers) and Neural Networks and Reservoir Computing (4 papers). Sijie Ma is often cited by papers focused on Advanced Memory and Neural Computing (9 papers), CCD and CMOS Imaging Sensors (4 papers) and Neural Networks and Reservoir Computing (4 papers). Sijie Ma collaborates with scholars based in Hong Kong, China and South Korea. Sijie Ma's co-authors include Yang Chai, Tianqing Wan, Yue Zhou, Qiao Li, Zhaoqing Wang, Yuhui He, Fuwei Zhuge, Xiangshui Miao, Jian‐Min Yan and Fuyou Liao and has published in prestigious journals such as Advanced Materials, Nano Letters and ACS Nano.

In The Last Decade

Sijie Ma

9 papers receiving 642 citations

Hit Papers

In‐Sensor Computing: Materials, Devices, and Integration ... 2022 2026 2023 2024 2022 2023 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sijie Ma Hong Kong 9 570 179 141 123 95 12 654
Guangdi Feng China 11 758 1.3× 300 1.7× 158 1.1× 173 1.4× 144 1.5× 22 829
Je‐Jun Lee South Korea 9 581 1.0× 208 1.2× 124 0.9× 210 1.7× 114 1.2× 17 649
Yulong Huang China 12 466 0.8× 205 1.1× 97 0.7× 67 0.5× 158 1.7× 17 545
Spyros Stathopoulos United Kingdom 14 863 1.5× 392 2.2× 81 0.6× 107 0.9× 120 1.3× 59 946
Zefan Li China 9 626 1.1× 304 1.7× 133 0.9× 85 0.7× 151 1.6× 13 819
Furqan Zahoor India 13 1.0k 1.8× 201 1.1× 72 0.5× 198 1.6× 206 2.2× 37 1.1k
Joanna Symonowicz United Kingdom 7 772 1.4× 200 1.1× 188 1.3× 306 2.5× 105 1.1× 9 944
Yanyun Ren China 13 787 1.4× 331 1.8× 180 1.3× 141 1.1× 229 2.4× 24 957
Beom Jin Kim South Korea 10 745 1.3× 266 1.5× 182 1.3× 235 1.9× 154 1.6× 17 1000

Countries citing papers authored by Sijie Ma

Since Specialization
Citations

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

Fields of papers citing papers by Sijie Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sijie Ma

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

All Works

12 of 12 papers shown
1.
Zeng, Guang, Sijie Ma, Tianqing Wan, et al.. (2025). Fabrication and characterization of optoelectronic in-sensor computing devices. Nature Protocols.
2.
Wan, Tianqing, Zhihang Xu, Chaoyi Zhu, et al.. (2025). Transferable Highly Crystalline Perovskite Ferroelectrics for Low-Power Memory. ACS Nano. 19(41). 36313–36322.
3.
Ma, Sijie, et al.. (2025). Magneto-optics enhances in-memory computing. Nature Photonics. 19(1). 9–10.
4.
Wang, Zhaoqing, Tianqing Wan, Sijie Ma, & Yang Chai. (2024). Multidimensional vision sensors for information processing. Nature Nanotechnology. 19(7). 919–930. 64 indexed citations
5.
Zhu, Chaoyi, Sijie Ma, Zhaoqing Wang, et al.. (2024). Optoelectronic Devices for In‐Sensor Computing. Advanced Materials. 37(23). e2407476–e2407476. 45 indexed citations
6.
Ma, Sijie, Yue Zhou, Tianqing Wan, et al.. (2024). Bioinspired In-Sensor Multimodal Fusion for Enhanced Spatial and Spatiotemporal Association. Nano Letters. 24(23). 7091–7099. 19 indexed citations
7.
Zhou, Yue, Jiawei Fu, Zirui Chen, et al.. (2023). Computational event-driven vision sensors for in-sensor spiking neural networks. Nature Electronics. 6(11). 870–878. 149 indexed citations breakdown →
8.
Wan, Tianqing, Fuyou Liao, Beom Jin Kim, et al.. (2023). Highly Trustworthy In-Sensor Cryptography for Image Encryption and Authentication. ACS Nano. 17(11). 10291–10299. 49 indexed citations
9.
Wan, Tianqing, et al.. (2022). In‐Sensor Computing: Materials, Devices, and Integration Technologies. Advanced Materials. 35(37). e2203830–e2203830. 184 indexed citations breakdown →
10.
Zhou, Yue, Fuwei Zhuge, Jianmiao Guo, et al.. (2022). A Reconfigurable Two‐WSe2‐Transistor Synaptic Cell for Reinforcement Learning. Advanced Materials. 34(48). e2107754–e2107754. 90 indexed citations
11.
Zhou, Yue, Jiawei Fu, Tianqing Wan, et al.. (2022). A 2T2R1C vision cell with 140 dB dynamic range and event-driven characteristics for in-sensor spiking neural network. 2022 International Electron Devices Meeting (IEDM). 31.4.1–31.4.4. 8 indexed citations
12.
Wan, Tianqing, et al.. (2021). Neuromorphic sensory computing. Science China Information Sciences. 65(4). 46 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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