Ying Mao

1.4k total citations · 1 hit paper
51 papers, 707 citations indexed

About

Ying Mao is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Ying Mao has authored 51 papers receiving a total of 707 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computer Networks and Communications, 19 papers in Information Systems and 14 papers in Artificial Intelligence. Recurrent topics in Ying Mao's work include IoT and Edge/Fog Computing (17 papers), Cloud Computing and Resource Management (16 papers) and Quantum Computing Algorithms and Architecture (11 papers). Ying Mao is often cited by papers focused on IoT and Edge/Fog Computing (17 papers), Cloud Computing and Resource Management (16 papers) and Quantum Computing Algorithms and Architecture (11 papers). Ying Mao collaborates with scholars based in United States, China and Netherlands. Ying Mao's co-authors include Long Cheng, Qingzhi Liu, Cong Liu, John Murphy, Jinwei Liu, Xuan Chen, Bo Sheng, Jiayin Wang, Deyong Sun and Muhammad Bilal and has published in prestigious journals such as Journal of Hydrology, Optics Express and Expert Systems with Applications.

In The Last Decade

Ying Mao

47 papers receiving 673 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

2020 214 citations Xuan Chen, Long Cheng et al. IEEE Systems Journal profile →

Peers

Ying Mao

CNC

IS

AI

EEE

CVPR

Zichen Xu China

Xiaohui Wei China

Hongmin Lu China

Chih‐Hung Wu Taiwan

Mustapha Lebbah France

Seunghyun Yoon South Korea

Takuo Watanabe Japan

Neelam Sharma India

Xiaoxin Chen China

Longjie Li China

Zichen Xu China

Ying Mao

315 ×0.7

CNC

263 ×0.7

IS

153 ×0.9

AI

112 ×1.4

EEE

64 ×0.8

CVPR

Citations per year, relative to Ying Mao Ying Mao (= 1×) peers Zichen Xu

Countries citing papers authored by Ying Mao

Since Specialization

Citations

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

Fields of papers citing papers by Ying Mao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ying Mao

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Saurabh, Nishant, et al.. (2025). Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems. IEEE Systems Journal. 19(2). 471–482.

2.

Li, Guodong, et al.. (2025). Quantum neural network classifier with differential privacy. Physica Scripta. 100(3). 35109–35109. 1 indexed citations

3.

Stein, Samuel, Samuel Yen-Chi Chen, Ang Li, et al.. (2024). A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity. IEEE Transactions on Quantum Engineering. 5. 1–14. 8 indexed citations

4.

Liu, Qingzhi, Chenglu Jin, Xiaohan Zhou, et al.. (2024). Privacy and Integrity Protection for IoT Multimodal Data Using Machine Learning and Blockchain. ACM Transactions on Multimedia Computing Communications and Applications. 20(6). 1–18. 6 indexed citations

5.

Li, Guodong, et al.. (2024). Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states. iScience. 27(6). 109836–109836. 3 indexed citations

6.

Zhang, Xueqing, Junkai Zhang, Ying Mao, et al.. (2024). Demo: Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning. 1431–1434. 1 indexed citations

7.

Guan, Qiang, et al.. (2023). QuantumEyes: Towards Better Interpretability of Quantum Circuits. IEEE Transactions on Visualization and Computer Graphics. 30(9). 6321–6333. 4 indexed citations

8.

Zhang, Jingwei, Long Cheng, Cong Liu, Zhiming Zhao, & Ying Mao. (2023). Cost-aware scheduling systems for real-time workflows in cloud: An approach based on Genetic Algorithm and Deep Reinforcement Learning. Expert Systems with Applications. 234. 120972–120972. 34 indexed citations

9.

Guan, Qiang, et al.. (2023). VENUS: A Geometrical Representation for Quantum State Visualization. Computer Graphics Forum. 42(3). 247–258. 7 indexed citations

10.

Mao, Ying, et al.. (2022). Elastic Resource Management for Deep Learning Applications in a Container Cluster. IEEE Transactions on Cloud Computing. 11(2). 2204–2216. 12 indexed citations

11.

Mao, Ying, et al.. (2022). Differentiate Quality of Experience Scheduling for Deep Learning Inferences With Docker Containers in the Cloud. IEEE Transactions on Cloud Computing. 11(2). 1667–1677. 12 indexed citations

12.

Wang, Yong, et al.. (2022). VACSEN : A V isualization A pproa c h for Noi s e Awaren e ss in Qua n tum Computing. IEEE Transactions on Visualization and Computer Graphics. 29(1). 462–472. 11 indexed citations

13.

Mao, Ying, et al.. (2022). Performance Evaluation of Resource Management Schemes for Cloud Native Platforms with Computing Containers. Data Archiving and Networked Services (DANS). abs/2010.10350. 414–415. 1 indexed citations

14.

Liu, Qingzhi, et al.. (2021). Deep Reinforcement Learning for Load-Balancing Aware Network Control in IoT Edge Systems. IEEE Transactions on Parallel and Distributed Systems. 33(6). 1491–1502. 62 indexed citations

15.

Mao, Ying, et al.. (2021). Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster. IEEE Systems Journal. 16(3). 3770–3781. 21 indexed citations

16.

Cheng, Long, Ying Wang, Qingzhi Liu, et al.. (2021). Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers. IEEE Transactions on Parallel and Distributed Systems. 32(6). 1494–1510. 29 indexed citations

17.

Chen, Xuan, Long Cheng, Cong Liu, et al.. (2020). A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems. IEEE Systems Journal. 14(3). 3117–3128. 214 indexed citations breakdown →

18.

Mao, Ying, et al.. (2019). Progress-based Container Scheduling for Short-lived Applications in a Kubernetes Cluster. 278–287. 25 indexed citations

19.

Hou, Yantian, et al.. (2019). Edge-Assisted Image Processing with Joint Optimization of Responding and Placement Strategy. Scholar Works (Boise State University). abs 1512 2325. 1241–1248. 1 indexed citations

20.

Wang, Jiayin, Teng Wang, Zhengyu Yang, et al.. (2017). SEINA: A stealthy and effective internal attack in Hadoop systems. IEEE Conference Proceedings. 2017. 530. 2 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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