Malu Zhang
Impact in
- Cognitive Neuroscience top 2%
- Neural dynamics and brain function
- EEG and Brain-Computer Interfaces
- Artificial Intelligence top 2%
- Neural Networks and Reservoir Computing
- Neural Networks and Applications
Papers in
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- Advanced Memory and Neural Computing 57
- Ferroelectric and Negative Capacitance Devices 20
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- Neural Networks and Reservoir Computing 23
- Neural Networks and Applications 10
- Topic Modeling 5
- Co-authors
- Haizhou Li (30 shared papers)Jibin Wu (27 shared papers)Kay Chen Tan (9 shared papers)Hong Qu (23 shared papers)Yansong Chua (12 shared papers)Xiurui Xie (14 shared papers)Zihan Pan (9 shared papers)Guoqi Li (4 shared papers)
In The Last Decade
Malu Zhang
72 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 90
- Cognitive Neuroscience 665
- Artificial Intelligence 503
- Electrical and Electronic Engineering 824
- Signal Processing 138
- Computational Mathematics 5
Countries citing papers authored by Malu Zhang
This map shows the geographic impact of Malu Zhang'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 Malu Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Malu Zhang more than expected).
Fields of papers citing papers by Malu Zhang
This network shows the impact of papers produced by Malu Zhang. 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 Malu Zhang. The network helps show where Malu Zhang may publish in the future.
Co-authors
The 25 scholars most cited alongside Malu Zhang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 82 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 120 | |
| 2 | 2021 | 95 | |
| 3 | 2020 | 91 | |
| 4 | 2018 | 87 | |
| 5 | 2016 | 52 | |
| 6 | 2020 | 51 | |
| 7 | 2023 | 45 | |
| 8 | 2019 | 36 | |
| 9 | 2024 | 33 | |
| 10 | 2020 | 33 | |
| 11 | 2020 | 31 | |
| 12 | 2022 | 31 | |
| 13 | 2019 | 30 | |
| 14 | 2023 | 29 | |
| 15 | 2022 | 26 | |
| 16 | 2021 | 25 | |
| 17 | 2017 | 20 | |
| 18 | 2014 | 16 | |
| 19 | Spike-Timing-Dependent Back Propagation in Deep Spiking Neural Networks | 2020 | 15 |
| 20 | 2023 | 14 |
About Malu Zhang
Malu Zhang is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Cognitive Neuroscience, Signal Processing and Computer Vision and Pattern Recognition, having authored 82 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (57 papers), Neural dynamics and brain function (41 papers), Neural Networks and Reservoir Computing (23 papers), Ferroelectric and Negative Capacitance Devices (20 papers), Neural Networks and Applications (10 papers), EEG and Brain-Computer Interfaces (6 papers), Speech and Audio Processing (6 papers) and Topic Modeling (5 papers). The work is most often cited by research in Cognitive Neuroscience (665 citations), Artificial Intelligence (503 citations), Electrical and Electronic Engineering (824 citations), Signal Processing (138 citations) and Computational Mathematics (5 citations). Malu Zhang has collaborated with scholars based in China, Singapore and Hong Kong. Frequent co-authors include Haizhou Li, Jibin Wu, Kay Chen Tan, Hong Qu, Yansong Chua, Xiurui Xie, Zihan Pan, Guoqi Li, Jiadong Wang and Yuchen Wang. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Knowledge-Based Systems, Neurocomputing and Frontiers in Neuroscience.
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.