Jonathan Ullman

2.9k total citations
47 papers, 749 citations indexed

About

Jonathan Ullman is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, Jonathan Ullman has authored 47 papers receiving a total of 749 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 10 papers in Management Science and Operations Research and 9 papers in Computational Theory and Mathematics. Recurrent topics in Jonathan Ullman's work include Privacy-Preserving Technologies in Data (35 papers), Cryptography and Data Security (21 papers) and Privacy, Security, and Data Protection (7 papers). Jonathan Ullman is often cited by papers focused on Privacy-Preserving Technologies in Data (35 papers), Cryptography and Data Security (21 papers) and Privacy, Security, and Data Protection (7 papers). Jonathan Ullman collaborates with scholars based in United States, Mexico and Israel. Jonathan Ullman's co-authors include Adam Smith, Thomas Steinke, Aaron Roth, Cynthia Dwork, Moritz Hardt, Salil Vadhan, Mallesh M. Pai, Michael Kearns, Anupam Gupta and Mark Rudelson and has published in prestigious journals such as SHILAP Revista de lepidopterología, American Economic Review and IEEE Transactions on Information Theory.

In The Last Decade

Jonathan Ullman

44 papers receiving 695 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan Ullman United States 15 588 122 113 95 93 47 749
Xiaorui Sun United States 10 164 0.3× 40 0.3× 62 0.5× 143 1.5× 126 1.4× 31 614
Per Austrin Sweden 12 808 1.4× 220 1.8× 283 2.5× 108 1.1× 133 1.4× 33 1.1k
Abhradeep Thakurta United States 19 1.3k 2.2× 224 1.8× 39 0.3× 118 1.2× 96 1.0× 40 1.4k
Michael Yu Zhu United States 3 509 0.9× 118 1.0× 40 0.4× 47 0.5× 58 0.6× 7 580
Josh Attenberg United States 12 536 0.9× 34 0.3× 26 0.2× 73 0.8× 165 1.8× 20 865
Or Sheffet United States 10 285 0.5× 81 0.7× 95 0.8× 151 1.6× 39 0.4× 25 498
Giulia Fanti United States 18 458 0.8× 92 0.8× 27 0.2× 18 0.2× 241 2.6× 46 780
Mukund Sundararajan United States 10 429 0.7× 103 0.8× 34 0.3× 67 0.7× 89 1.0× 13 539
Youze Tang Singapore 5 282 0.5× 92 0.8× 68 0.6× 98 1.0× 282 3.0× 5 1.1k

Countries citing papers authored by Jonathan Ullman

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Jonathan Ullman

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

All Works

20 of 20 papers shown
1.
Jagielski, Matthew, Stanley Wu, Alina Oprea, Jonathan Ullman, & Roxana Geambasu. (2023). How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proceedings on Privacy Enhancing Technologies. 2023(3). 211–232. 1 indexed citations
2.
Crnovrsanin, Tarik, et al.. (2023). Investigating the Visual Utility of Differentially Private Scatterplots. IEEE Transactions on Visualization and Computer Graphics. 30(8). 5370–5385. 5 indexed citations
3.
Liu, Terrance, et al.. (2021). Leveraging Public Data for Practical Private Query Release. International Conference on Machine Learning. 6968–6977. 7 indexed citations
4.
Jagielski, Matthew, Jonathan Ullman, & Alina Oprea. (2020). Auditing Differentially Private Machine Learning: How Private is Private SGD?. Neural Information Processing Systems. 33. 22205–22216. 3 indexed citations
5.
Kamath, Gautam, et al.. (2020). Private Mean Estimation of Heavy-Tailed Distributions. Conference on Learning Theory. 2204–2235.
6.
Ullman, Jonathan, et al.. (2019). Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. Neural Information Processing Systems. 32. 3765–3775. 5 indexed citations
7.
Smith, Adam, et al.. (2018). Distributed Differential Privacy via Mixnets.. arXiv (Cornell University). 3 indexed citations
8.
Ullman, Jonathan, Adam Smith, Kobbi Nissim, Uri Stemmer, & Thomas Steinke. (2018). The Limits of Post-Selection Generalization. Neural Information Processing Systems. 31. 6400–6409. 2 indexed citations
9.
Dwork, Cynthia & Jonathan Ullman. (2018). The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy. SHILAP Revista de lepidopterología. 8(1). 8 indexed citations
10.
Bun, Mark, Thomas Steinke, & Jonathan Ullman. (2017). Make up your mind: the price of online queries in differential privacy. arXiv (Cornell University). 1306–1325. 3 indexed citations
11.
Steinke, Thomas & Jonathan Ullman. (2017). Tight Lower Bounds for Differentially Private Selection. 552–563. 7 indexed citations
12.
Indyk, Piotr, Sepideh Mahabadi, Ronitt Rubinfeld, et al.. (2017). Fractional Set Cover in the Streaming Model. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 2 indexed citations
13.
Pai, Mallesh M., Aaron Roth, & Jonathan Ullman. (2016). An Antifolk Theorem for Large Repeated Games. 5(2). 1–20. 5 indexed citations
14.
Rogers, Ryan, Aaron Roth, Jonathan Ullman, & Salil Vadhan. (2016). Privacy Odometers and Filters: Pay-as-you-Go Composition. arXiv (Cornell University). 29. 1929–1937. 19 indexed citations
15.
Ullman, Jonathan. (2016). Answering $n^2+o(1)$ Counting Queries with Differential Privacy is Hard. SIAM Journal on Computing. 45(2). 473–496. 7 indexed citations
16.
O’Brien, David, Jonathan Ullman, Micah Altman, et al.. (2015). Integrating Approaches to Privacy Across the Research Lifecycle: When Is Information Purely Public?. SSRN Electronic Journal. 3 indexed citations
17.
Hardt, Moritz & Jonathan Ullman. (2014). Preventing False Discovery in Interactive Data Analysis Is Hard. 454–463. 29 indexed citations
18.
Wood, Alexandra, Micah Altman, Alan F. Karr, et al.. (2014). Integrating Approaches to Privacy Across the Research Lifecycle: Long-Term Longitudinal Studies. SSRN Electronic Journal. 2 indexed citations
19.
Gupta, Anupam, Moritz Hardt, Aaron Roth, & Jonathan Ullman. (2013). Privately Releasing Conjunctions and the Statistical Query Barrier. SIAM Journal on Computing. 42(4). 1494–1520. 14 indexed citations
20.
Kearns, Michael, Mallesh M. Pai, Aaron Roth, & Jonathan Ullman. (2012). Private Equilibrium Release, Large Games, and No-Regret Learning. 3 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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