Randolph Yao

1.1k total citations · 1 hit paper
11 papers, 677 citations indexed

About

Randolph Yao is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Randolph Yao has authored 11 papers receiving a total of 677 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Networks and Communications, 9 papers in Information Systems and 5 papers in Artificial Intelligence. Recurrent topics in Randolph Yao's work include Cloud Computing and Resource Management (9 papers), IoT and Edge/Fog Computing (5 papers) and Software System Performance and Reliability (5 papers). Randolph Yao is often cited by papers focused on Cloud Computing and Resource Management (9 papers), IoT and Edge/Fog Computing (5 papers) and Software System Performance and Reliability (5 papers). Randolph Yao collaborates with scholars based in United States, China and Australia. Randolph Yao's co-authors include Yingnong Dang, Murali Chintalapati, Qingwei Lin, Hongyu Zhang, Dongmei Zhang, Yong Xu, Jian–Guang Lou, Bo Qiao, Xinsheng Yang and Junjie Chen and has published in prestigious journals such as Networked Systems Design and Implementation, Figshare and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Randolph Yao

11 papers receiving 666 citations

Hit Papers

Robust log-based anomaly detection on unstable log data 2019 2026 2021 2023 2019 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Randolph Yao United States 7 604 353 261 112 21 11 677
Hiep Nguyen United States 9 569 0.9× 173 0.5× 402 1.5× 78 0.7× 28 1.3× 14 612
Fabian Brosig Germany 12 423 0.7× 236 0.7× 323 1.2× 120 1.1× 5 0.2× 22 471
Emre Kıcıman United States 5 674 1.1× 199 0.6× 423 1.6× 297 2.7× 12 0.6× 12 783
Risto Vaarandi Estonia 11 702 1.2× 362 1.0× 346 1.3× 161 1.4× 83 4.0× 26 783
Weibin Meng China 15 835 1.4× 547 1.5× 284 1.1× 159 1.4× 76 3.6× 32 931
Shimin Tao China 10 526 0.9× 479 1.4× 178 0.7× 110 1.0× 43 2.0× 63 739
Soila Kavulya United States 9 541 0.9× 129 0.4× 439 1.7× 68 0.6× 30 1.4× 25 580
Gautam Kar United States 10 498 0.8× 153 0.4× 416 1.6× 46 0.4× 18 0.9× 25 580
Pu Zhao China 13 268 0.4× 179 0.5× 189 0.7× 63 0.6× 12 0.6× 39 399

Countries citing papers authored by Randolph Yao

Since Specialization
Citations

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

Fields of papers citing papers by Randolph Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Randolph Yao

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

All Works

11 of 11 papers shown
1.
Zhang, Chaoyun, Randolph Yao, Si Qin, et al.. (2024). Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud Infrastructure. 361–379. 1 indexed citations
2.
Ma, Minghua, Yudong Liu, Si Qin, et al.. (2023). CODEC: Cost-Effective Duration Prediction System for Deadline Scheduling in the Cloud. 298–308. 3 indexed citations
3.
Yao, Randolph, Chuan Luo, Bo Qiao, et al.. (2021). Infusing ML into VM Provisioning in Cloud. 44–45. 1 indexed citations
4.
Luo, Chuan, Bo Qiao, Xin Chen, et al.. (2021). Correlation-Aware Heuristic Search for Intelligent Virtual Machine Provisioning in Cloud Systems. Proceedings of the AAAI Conference on Artificial Intelligence. 35(14). 12363–12372. 13 indexed citations
5.
Yao, Randolph, Yingnong Dang, Peng Huang, et al.. (2020). Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions.. 1155–1170. 5 indexed citations
6.
Luo, Chuan, Bo Qiao, Xin Chen, et al.. (2020). Intelligent Virtual Machine Provisioning in Cloud Computing. 1495–1502. 23 indexed citations
7.
Zhang, Xu, Yong Xu, Qingwei Lin, et al.. (2019). Robust log-based anomaly detection on unstable log data. 807–817. 401 indexed citations breakdown →
8.
Zhang, Qiao, Chuanxiong Guo, Yingnong Dang, et al.. (2018). Deepview: Virtual Disk Failure Diagnosis and Pattern Detection for Azure.. Networked Systems Design and Implementation. 519–532. 16 indexed citations
9.
Xu, Yong, Kaixin Sui, Randolph Yao, et al.. (2018). Improving Service Availability of Cloud Systems by Predicting Disk Error.. Figshare. 481–494. 46 indexed citations
10.
Lin, Qingwei, Yingnong Dang, Hongyu Zhang, et al.. (2018). Predicting Node failure in cloud service systems. 480–490. 76 indexed citations
11.
Huang, Peng, Chuanxiong Guo, Lidong Zhou, et al.. (2017). Gray Failure. 150–155. 92 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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