Joshua R. Wang
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Computer Networks and Communications
- Management Science and Operations Research
- Computer Vision and Pattern Recognition
- Co-authors
- Tim RoughgardenBenjamin MoseleySergei VassilvitskiiHuacheng YuVirginia Vassilevska WilliamsRyan WilliamsGuru GuruganeshManish Purohit
- Topics
- Auction Theory and Applications (5 papers)Complexity and Algorithms in Graphs (4 papers)Advanced Graph Theory Research (3 papers)
- Cited by
- Computational MathematicsManagement Science and Operations ResearchComputational Theory and Mathematics
- Journals
- Journal of the ACMSymposium on Discrete AlgorithmsNeural Information Processing Systems
- Partner nations
- United StatesIndia
In The Last Decade
Joshua R. Wang
12 papers receiving 128 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 48
- Computational Theory and Mathematics 41
- Computer Networks and Communications 37
- Management Science and Operations Research 35
- Computer Vision and Pattern Recognition 24
Countries citing papers authored by Joshua R. Wang
This map shows the geographic impact of Joshua R. Wang'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 Joshua R. Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua R. Wang more than expected).
Fields of papers citing papers by Joshua R. Wang
This network shows the impact of papers produced by Joshua R. Wang. 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 Joshua R. Wang. The network helps show where Joshua R. Wang may publish in the future.
Co-authorship network of co-authors of Joshua R. Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Joshua R. Wang. A scholar is included among the top collaborators of Joshua R. Wang 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 Joshua R. Wang. Joshua R. Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 10 | |
| 4 | 4 | |
| 5 | Efficient Rematerialization for Deep Networks | 9 |
| 6 | 9 | |
| 7 | 12 | |
| 8 | Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search | 34 |
| 9 | 5 | |
| 10 | 11 | |
| 11 | 13 | |
| 12 | 10 | |
| 13 | 12 |
About Joshua R. Wang
Joshua R. Wang is a scholar working on Management Science and Operations Research, Computer Graphics and Computer-Aided Design and Computational Theory and Mathematics, having authored 13 papers that have together received 131 indexed citations. Recurring topics across this work include Auction Theory and Applications (5 papers), Complexity and Algorithms in Graphs (4 papers) and Advanced Graph Theory Research (3 papers). The work is most often cited by research in Computational Mathematics (2 citations), Management Science and Operations Research (35 citations) and Computational Theory and Mathematics (41 citations). Joshua R. Wang has collaborated with scholars based in United States and India. Frequent co-authors include Tim Roughgarden, Benjamin Moseley, Sergei Vassilvitskii, Huacheng Yu, Virginia Vassilevska Williams, Ryan Williams, Guru Guruganesh, Manish Purohit, Zoya Svitkina and Erik Vee. Their work appears in journals such as Journal of the ACM, Symposium on Discrete Algorithms and Neural Information Processing Systems.
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.