World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
4031
World Ranking
14075
National Ranking
5583

Overview

Jonathan Ullman is affiliated with Northeastern University in the United States. Their work primarily resides within the field of Computer Science, with a particular focus on Artificial Intelligence, which comprises the majority of their research output. Other notable subfields include Statistics and Probability, Computational Theory and Mathematics, Epidemiology, and Health.

The research conducted by Ullman covers a range of topics. Key areas include Privacy-Preserving Technologies in Data, Cryptography and Data Security, Adversarial Robustness in Machine Learning, Complexity and Algorithms in Graphs, Data-Driven Disease Surveillance, Stochastic Gradient Optimization Techniques, and Health disparities and outcomes.

Among their recent publications are:

  • Auditing Differentially Private Machine Learning: How Private is Private SGD? (2020), published in arXiv (Cornell University)
  • Local Differential Privacy for Evolving Data (2020), published in Journal of Privacy and Confidentiality
  • CoinPress: Practical Private Mean and Covariance Estimation (2020), published in arXiv (Cornell University)
  • A Primer on Private Statistics (2020), published in arXiv (Cornell University)
  • Private Mean Estimation of Heavy-Tailed Distributions (2020), published in arXiv (Cornell University)

Frequent co-authors of Jonathan Ullman include Gautam Kamath, Thomas Steinke, Alina Oprea, Argyris Mouzakis, and Adam Smith.

Ullman's work has appeared predominantly in arXiv, followed by contributions to the Journal of Privacy and Confidentiality, Proceedings on Privacy Enhancing Technologies, IEEE Transactions on Visualization and Computer Graphics, and the Journal of Cryptology.

Best Publications

  • Privately Releasing Conjunctions and the Statistical Query Barrier

    Anupam Gupta;Moritz Hardt;Aaron Roth;Jonathan R. Ullman

  • Exposed! A Survey of Attacks on Private Data

    Cynthia Dwork;Adam Smith;Thomas Steinke;Jonathan Ullman

  • Algorithmic stability for adaptive data analysis

    Raef Bassily;Kobbi Nissim;Adam Smith;Thomas Steinke

  • Distributed Differential Privacy via Shuffling

    Albert Cheu;Adam D. Smith;Jonathan R. Ullman;David Zeber

  • Iterative constructions and private data release

    Anupam Gupta;Aaron Roth;Jonathan Ullman

  • Robust Traceability from Trace Amounts

    Cynthia Dwork;Adam Smith;Thomas Steinke;Jonathan Ullman

  • Privately releasing conjunctions and the statistical query barrier

    Anupam Gupta;Moritz Hardt;Aaron Roth;Jonathan Ullman

  • Fingerprinting Codes and the Price of Approximate Differential Privacy

    Mark Bun;Jonathan R. Ullman;Salil P. Vadhan

  • Mechanism Design in Large Games: Incentives and Privacy

    Michael Kearns;Mallesh M. Pai;Aaron Roth;Jonathan Ullman

  • Mechanism design in large games: incentives and privacy

    Michael Kearns;Mallesh Pai;Aaron Roth;Jonathan Ullman

  • PCPs and the hardness of generating private synthetic data

    Jonathan Ullman;Salil Vadhan

  • The price of privately releasing contingency tables and the spectra of random matrices with correlated rows

    Shiva Prasad Kasiviswanathan;Mark Rudelson;Adam Smith;Jonathan Ullman

  • Fingerprinting codes and the price of approximate differential privacy

    Mark Bun;Jonathan Ullman;Salil Vadhan

  • Preventing False Discovery in Interactive Data Analysis Is Hard

    Moritz Hardt;Jonathan Ullman

  • Interactive fingerprinting codes and the hardness of preventing false discovery

    Thomas Steinke;Jonathan Ullman

  • Faster algorithms for privately releasing marginals

    Justin Thaler;Jonathan Ullman;Salil Vadhan

  • On the capabilities of codes to correct synchronization errors

    J. Ullman

  • Auditing Differentially Private Machine Learning: How Private is Private SGD?

    Matthew Jagielski;Jonathan R. Ullman;Alina Oprea

  • Between Pure and Approximate Differential Privacy

    Thomas Steinke;Jonathan R. Ullman

  • Auditing Differentially Private Machine Learning: How Private is Private SGD?

    Matthew Jagielski;Jonathan Ullman;Alina Oprea

  • Differentially Private Fair Learning

    Matthew Jagielski;Michael J. Kearns;Jieming Mao;Alina Oprea

  • Manipulation Attacks in Local Differential Privacy

    Albert Cheu;Adam Smith;Jonathan Ullman

  • Privacy odometers and filters: pay-as-you-go composition

    Ryan Rogers;Aaron Roth;Jonathan Ullman;Salil Vadhan

  • Answering $n^2+o(1)$ Counting Queries with Differential Privacy is Hard

    Jonathan R. Ullman

  • Near-optimal, single-synchronization-error-correcting code

    J. Ullman

  • Watch and learn: optimizing from revealed preferences feedback

    Aaron Roth;Jonathan Ullman;Zhiwei Steven Wu

  • Private Mean Estimation of Heavy-Tailed Distributions

    Gautam Kamath;Vikrant Singhal;Jonathan R. Ullman

  • CoinPress: Practical Private Mean and Covariance Estimation

    Sourav Biswas;Yihe Dong;Gautam Kamath;Jonathan R. Ullman

Frequent Co-Authors

Aaron Roth
Aaron Roth University of Pennsylvania
Adam Smith
Adam Smith Boston University
Salil P. Vadhan
Salil P. Vadhan Harvard University
Kobbi Nissim
Kobbi Nissim Georgetown University
Michael Kearns
Michael Kearns University of Pennsylvania
Moritz Hardt
Moritz Hardt Max Planck Institute for Intelligent Systems
Anupam Gupta
Anupam Gupta Carnegie Mellon University
Jeffrey D. Ullman
Jeffrey D. Ullman Stanford University
Michael Mitzenmacher
Michael Mitzenmacher Harvard University
Alina Oprea
Alina Oprea Northeastern University

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