Dakai Zhu

6.8k total citations
139 papers, 3.8k citations indexed

About

Dakai Zhu is a scholar working on Hardware and Architecture, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Dakai Zhu has authored 139 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Hardware and Architecture, 59 papers in Computer Networks and Communications and 33 papers in Electrical and Electronic Engineering. Recurrent topics in Dakai Zhu's work include Real-Time Systems Scheduling (65 papers), Parallel Computing and Optimization Techniques (59 papers) and Distributed systems and fault tolerance (27 papers). Dakai Zhu is often cited by papers focused on Real-Time Systems Scheduling (65 papers), Parallel Computing and Optimization Techniques (59 papers) and Distributed systems and fault tolerance (27 papers). Dakai Zhu collaborates with scholars based in United States, China and Canada. Dakai Zhu's co-authors include Hakan Aydın, Rami Melhem, Daniel Mossé, Baoxian Zhao, Hakan Aydın, Bruce R. Childers, Hang Su, Mohammad A. Haque, Christopher I. Amos and Kim‐Kwang Raymond Choo and has published in prestigious journals such as PLoS ONE, Human Molecular Genetics and International Journal of Cancer.

In The Last Decade

Dakai Zhu

132 papers receiving 3.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dakai Zhu United States 34 2.5k 2.0k 795 625 229 139 3.8k
Andrea Acquaviva Italy 32 696 0.3× 992 0.5× 1.4k 1.7× 186 0.3× 229 1.0× 208 3.1k
W.K. Fuchs United States 30 1.9k 0.8× 1.4k 0.7× 1.3k 1.7× 100 0.2× 94 0.4× 166 2.9k
Sun‐Yuan Hsieh Taiwan 39 924 0.4× 3.5k 1.8× 1.7k 2.2× 289 0.5× 130 0.6× 235 4.1k
Shan Lu United States 40 2.4k 1.0× 3.8k 1.9× 794 1.0× 2.7k 4.4× 98 0.4× 169 6.3k
Hans Hansson Sweden 28 1.1k 0.4× 861 0.4× 203 0.3× 242 0.4× 179 0.8× 151 2.9k
Marco D. Santambrogio Italy 26 1.6k 0.7× 1.5k 0.7× 831 1.0× 556 0.9× 143 0.6× 338 3.0k
Shaolei Ren United States 33 233 0.1× 2.0k 1.0× 1.5k 1.9× 1.3k 2.0× 74 0.3× 193 3.6k
Wenguang Chen China 32 1.1k 0.4× 1.6k 0.8× 420 0.5× 1.1k 1.7× 64 0.3× 191 3.2k
Kunal Agrawal United States 22 1.2k 0.5× 1.2k 0.6× 65 0.1× 273 0.4× 30 0.1× 137 1.9k
Xuehai Zhou China 25 711 0.3× 824 0.4× 839 1.1× 318 0.5× 130 0.6× 230 2.5k

Countries citing papers authored by Dakai Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Dakai Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dakai Zhu

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

All Works

20 of 20 papers shown
1.
Alaeddini, Adel, et al.. (2024). Structured segment rescaling with Gaussian processes for parameter efficient ConvNets. Journal of Systems Architecture. 154. 103246–103246.
2.
Xie, Mimi, et al.. (2024). Real-time intelligent on-device monitoring of heart rate variability with PPG sensors. Journal of Systems Architecture. 154. 103240–103240.
3.
Zheng, Xi, Aloysius K. Mok, Ružica Piskač, et al.. (2024). Testing Learning-Enabled Cyber-Physical Systems with Large-Language Models: A Formal Approach. 467–471. 3 indexed citations
4.
Wang, Zhiwei, et al.. (2024). An intelligent assistive driving solution based on smartphone for power wheelchair mobility. Journal of Systems Architecture. 149. 103105–103105.
5.
Pérez-Díaz, Jesús Arturo, Ismael Amezcua Valdovinos, Kim‐Kwang Raymond Choo, & Dakai Zhu. (2020). A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning. IEEE Access. 8. 155859–155872. 207 indexed citations
6.
Ji, Xuemei, Siting Li, Xiangjun Xiao, et al.. (2020). A new efficient method to detect genetic interactions for lung cancer GWAS. BMC Medical Genomics. 13(1). 162–162. 6 indexed citations
7.
Wang, Xiaomeng, Hongliang Liu, Yinghui Xu, et al.. (2018). Genetic variants in the calcium signaling pathway genes are associated with cutaneous melanoma-specific survival. Carcinogenesis. 40(2). 279–288. 7 indexed citations
8.
Shi, Qiong, Hongliang Liu, Peng Han, et al.. (2017). Genetic Variants in WNT2B and BTRC Predict Melanoma Survival. Journal of Investigative Dermatology. 137(8). 1749–1756. 5 indexed citations
9.
Zhu, Dakai, et al.. (2017). Exploiting primary/backup mechanism for energy efficiency in dependable real-time systems. Journal of Systems Architecture. 78. 68–80. 32 indexed citations
10.
Liu, Shun, Yanru Wang, Hongliang Liu, et al.. (2017). Genetic variants in the genes encoding rho GTPases and related regulators predict cutaneous melanoma‐specific survival. International Journal of Cancer. 141(4). 721–730. 6 indexed citations
11.
Liu, Jing, Kenli Li, Dakai Zhu, Jian-Jun Han, & Keqin Li. (2016). Minimizing Cost of Scheduling Tasks on Heterogeneous Multicore Embedded Systems. ACM Transactions on Embedded Computing Systems. 16(2). 1–25. 29 indexed citations
12.
Zhu, Dakai, David C. Qian, Jinyoung Byun, et al.. (2015). Prediction of the gene expression in normal lung tissue by the gene expression in blood. BMC Medical Genomics. 8(1). 77–77. 18 indexed citations
13.
Ji, Xuemei, Weidong Zhang, Jiang Gui, et al.. (2014). Role of a Genetic Variant on the 15q25.1 Lung Cancer Susceptibility Locus in Smoking-Associated Nasopharyngeal Carcinoma. PLoS ONE. 9(10). e109036–e109036. 5 indexed citations
14.
Su, Hang & Dakai Zhu. (2013). An elastic mixed-criticality task model and its scheduling algorithm. Design, Automation, and Test in Europe. 147–152. 72 indexed citations
15.
Amos, Christopher I., et al.. (2013). Findings from the Peutz-Jeghers Syndrome Registry of Uruguay. PLoS ONE. 8(11). e79639–e79639. 18 indexed citations
16.
Tuna, Musaffe, Marcel Smid, Dakai Zhu, John W.M. Martens, & Christopher I. Amos. (2010). Association between Acquired Uniparental Disomy and Homozygous Mutations and HER2/ER/PR Status in Breast Cancer. PLoS ONE. 5(11). e15094–e15094. 17 indexed citations
17.
Kachroo, Sumesh, Margaret R. Spitz, Yun Xing, et al.. (2008). Trends in prevalence of prognostic factors and survival in lung cancer patients from 1985 to 2004 at a tertiary care center. Cancer Detection and Prevention. 32(2). 101–108. 12 indexed citations
18.
Gorlova, Olga Y., Lei Lei, Dakai Zhu, et al.. (2007). Imprinting detection by extending a regression-based QTL analysis method. Human Genetics. 122(2). 159–174. 6 indexed citations
19.
Zhu, Dakai, Rami Melhem, Daniel Mossé, & E. N. Mootaz Elnozahy. (2004). Analysis of an energy efficient optimistic TMR scheme. 559–568. 28 indexed citations
20.
Amos, Christopher I., Mariza de Andrade, & Dakai Zhu. (2001). Comparison of Multivariate Tests for Genetic Linkage. Human Heredity. 51(3). 133–144. 81 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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