Jonathan Ullman
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data 35
- Cryptography and Data Security 21
- Internet Traffic Analysis and Secure E-voting 5
- Machine Learning and Algorithms 3
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- Mobile Crowdsensing and Crowdsourcing 5
- Health Informatics top 10%
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- Auction Theory and Applications 6
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- Complexity and Algorithms in Graphs 6
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- Privacy, Security, and Data Protection 7
- Co-authors
- Adam SmithThomas SteinkeAaron RothCynthia DworkMoritz HardtSalil VadhanMallesh M. PaiMichael Kearns
- Journals
- SIAM Journal on Computing (3 papers)IEEE Transactions on Information Theory (2 papers)IEEE Transactions on Visualization and Computer Graphics (1 paper)
- Partner nations
- United StatesMexicoIsrael
In The Last Decade
Jonathan Ullman
44 papers receiving 695 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 588
- Computer Science Applications 66
- Health Informatics 12
- Management Science and Operations Research 95
- Computational Theory and Mathematics 113
Countries citing papers authored by Jonathan Ullman
This map shows the geographic impact of Jonathan Ullman'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 Jonathan Ullman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Ullman more than expected).
Fields of papers citing papers by Jonathan Ullman
This network shows the impact of papers produced by Jonathan Ullman. 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 Jonathan Ullman. The network helps show where Jonathan Ullman may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jonathan Ullman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 7 | |
| 2 | 2023 | 1 | |
| 3 | 2023 | 5 | |
| 4 | Leveraging Public Data for Practical Private Query Release | 2021 | 7 |
| 5 | Auditing Differentially Private Machine Learning: How Private is Private SGD? | 2020 | 3 |
| 6 | Private Mean Estimation of Heavy-Tailed Distributions | 2020 | 0 |
| 7 | Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy | 2019 | 5 |
| 8 | Distributed Differential Privacy via Mixnets. | 2018 | 3 |
| 9 | The Limits of Post-Selection Generalization | 2018 | 2 |
| 10 | 2018 | 8 | |
| 11 | 2017 | 3 | |
| 12 | 2017 | 7 | |
| 13 | 2016 | 5 | |
| 14 | 2016 | 19 | |
| 15 | 2016 | 50 | |
| 16 | 2015 | 3 | |
| 17 | 2014 | 29 | |
| 18 | 2014 | 2 | |
| 19 | 2013 | 14 | |
| 20 | Private Equilibrium Release, Large Games, and No-Regret Learning | 2012 | 3 |
About Jonathan Ullman
Jonathan Ullman is a scholar working on Artificial Intelligence, Computer Science Applications and Management Science and Operations Research, having authored 47 papers that have together received 749 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (35 papers), Cryptography and Data Security (21 papers), Privacy, Security, and Data Protection (7 papers), Auction Theory and Applications (6 papers), Complexity and Algorithms in Graphs (6 papers), Internet Traffic Analysis and Secure E-voting (5 papers), Mobile Crowdsensing and Crowdsourcing (5 papers) and Machine Learning and Algorithms (3 papers). The work is most often cited by research in Artificial Intelligence (588 citations), Computer Science Applications (66 citations) and Health Informatics (12 citations). Jonathan Ullman has collaborated with scholars based in United States, Mexico and Israel. Frequent co-authors include Adam Smith, Thomas Steinke, Aaron Roth, Cynthia Dwork, Moritz Hardt, Salil Vadhan, Mallesh M. Pai, Michael Kearns, Shiva Prasad Kasiviswanathan and Mark Rudelson. Their work appears in journals such as SIAM Journal on Computing, IEEE Transactions on Information Theory, IEEE Transactions on Visualization and Computer Graphics, Journal of Cryptology and American Economic Review.
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.