Jonathan Ullman
Associate Professor
Research interests
- Privacy
- Machine learning and statistics
- Cryptography
- Algorithms
Education
- PhD in Computer Science, Harvard University
- BSE in Computer Science, Princeton University
Biography
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.
Ullman has been recognized with an NSF CAREER award and the Ruth and Joel Spira Outstanding Teacher Award.
Recent publications
-
Algorithmic stability for adaptive data analysis
Citation: Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman. Algorithmic stability for adaptive data analysis. In Symposium on Theory of Computing (STOC’16), 2016 -
Interactive fingerprinting codes and the hardness of preventing false discovery
Citation: Thomas Steinke and Jonathan Ullman. Interactive fingerprinting codes and the hardness of preventing false discovery. In Proceedings of The 28th Conference on Learning Theory (COLT’15), 2015 -
Tight lower bounds for differentially private selection
Citation: Thomas Steinke and Jonathan Ullman. Tight lower bounds for differentially private selection. In IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS’17), 2017.
Related News
Current PhD Students
Previous PhD Students
-
Lydia Zakynthinou
-
Vikrant Singhal
-
Albert Cheu