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Jonathan Ullman
Differential Privacy
Statistical Validity In Privacy
Robustness In Machine Learning
Cryptographic Privacy Techniques
Fairness In Machine Learning
About
Jonathan Ullman is an associate professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.
Ullman's research centers on the foundations of privacy for machine learning and statistics, namely differential privacy and its surprising interplay with topics such as statistical validity, robustness, cryptography, and fairness. His background is in theoretical computer science, but his work spans algorithms, cryptography, machine learning, statistics, and security. His area of teaching includes algorithms and privacy for machine learning, and he is a member of the Theory Group, the Cybersecurity and Privacy Institute, and the Institute for Experiential AI.
Research Performance Summary
2008-2025
Active Research Span
First Recorded Paper
Query Release via Online Learning
Year:
2008
Citations:
0
Venue:
Encyclopedia of Algorithms
Latest Recorded Paper
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
Year:
2025
Citations:
0
Venue:
arXiv preprint arXiv:2506.16460
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Ullman
Smith
Steinke
Research Impact by Period
Papers
6
Citations
51
Avg. Citations / Paper
8.5
H-Index
2
Papers
29
Citations
1692
Avg. Citations / Paper
58.3
H-Index
19
Papers
42
Citations
4226
Avg. Citations / Paper
100.6
H-Index
25
Papers
13
Citations
1190
Avg. Citations / Paper
91.5
H-Index
11
Papers
1
Citations
0
Avg. Citations / Paper
0
H-Index
0
Contact and Professional Links
Detected Research Keywords
Private Mean Estimation
Adaptive Data Analysis
Approximate Differential Privacy
Local Differential Privacy
Preventing False Discovery
Interactive Data Analysis
Queries Differential Privacy
Lower Bounds Differentially
Bounds Differentially Private
Private Query Release