Huasha Zhao
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Hardware and Architecture top 10%
- Co-authors
- John CannyLuo SiBiye JiangQiong ZhangYe ChenYi YangXiaogang LiSurya Kallumadi
- Topics
- Caching and Content Delivery (4 papers)Graph Theory and Algorithms (3 papers)Cloud Computing and Resource Management (3 papers)
- Journals
- Theory and applications of categorieseScholarship (California Digital Library)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Huasha Zhao
12 papers receiving 159 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 107
- Information Systems 52
- Computer Networks and Communications 51
- Computer Vision and Pattern Recognition 47
- Hardware and Architecture 38
Countries citing papers authored by Huasha Zhao
This map shows the geographic impact of Huasha Zhao'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 Huasha Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Huasha Zhao more than expected).
Fields of papers citing papers by Huasha Zhao
This network shows the impact of papers produced by Huasha Zhao. 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 Huasha Zhao. The network helps show where Huasha Zhao may publish in the future.
Co-authorship network of co-authors of Huasha Zhao
This figure shows the co-authorship network connecting the top 25 collaborators of Huasha Zhao. A scholar is included among the top collaborators of Huasha Zhao 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 Huasha Zhao. Huasha Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 4 | |
| 3 | 9 | |
| 4 | Improve Neural Mention Detection and Classification via Enforced Training and Inference Consistency. | 1 |
| 5 | 9 | |
| 6 | 14 | |
| 7 | 14 | |
| 8 | 28 | |
| 9 | High Performance Machine Learning through Codesign and Rooflining | 8 |
| 10 | 15 | |
| 11 | 41 | |
| 12 | BIDMach: Large-scale Learning with Zero Memory Allocation | 20 |
About Huasha Zhao
Huasha Zhao is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications, having authored 12 papers that have together received 171 indexed citations. Recurring topics across this work include Caching and Content Delivery (4 papers), Graph Theory and Algorithms (3 papers) and Cloud Computing and Resource Management (3 papers). The work is most often cited by research in Computational Mathematics (14 citations), Hardware and Architecture (38 citations) and Artificial Intelligence (107 citations). Huasha Zhao has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include John Canny, Luo Si, Biye Jiang, Qiong Zhang, Ye Chen, Qiong Zhang, Yi Yang, Xiaogang Li, Qiong Zhang and Surya Kallumadi. Their work appears in journals such as Theory and applications of categories and eScholarship (California Digital Library).
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.