Matthew Joseph

765 total citations
6 papers, 44 citations indexed

About

Matthew Joseph is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Matthew Joseph has authored 6 papers receiving a total of 44 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computational Theory and Mathematics and 2 papers in Management Science and Operations Research. Recurrent topics in Matthew Joseph's work include Privacy-Preserving Technologies in Data (3 papers), Complexity and Algorithms in Graphs (2 papers) and Cryptography and Data Security (2 papers). Matthew Joseph is often cited by papers focused on Privacy-Preserving Technologies in Data (3 papers), Complexity and Algorithms in Graphs (2 papers) and Cryptography and Data Security (2 papers). Matthew Joseph collaborates with scholars based in United States, Australia and China. Matthew Joseph's co-authors include Michael Kearns, Aaron Roth, Jamie Morgenstern, Seth Neel, Shahin Jabbari, Xiangyang Zhu, Jieming Mao, Binglin Zhong and Kareem Amin and has published in prestigious journals such as ACM Transactions on Algorithms, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Matthew Joseph

6 papers receiving 43 citations

Peers

Matthew Joseph
Comparison fields: 5 of 21
  • Artificial Intelligence 33
  • Safety Research 18
  • Management Science and Operations Research 16
  • Computer Science Applications 6
  • Electrical and Electronic Engineering 6
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H S Chen China
Ayesha Bajwa Hong Kong View profile →
Citations per field, relative to Matthew Joseph
Matthew Joseph · 1×
Citations per year, relative to Matthew Joseph
Matthew Joseph · 1×

Countries citing papers authored by Matthew Joseph

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Joseph

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Joseph

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

All Works

6 of 6 papers shown
# Work Indexed citations
1 1
2
Pan-Private Uniformity Testing
1
3 12
4
Fair Learning in Markovian Environments.
7
5
Fairness in learning: classic and contextual bandits
21
6 2

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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