Posts by Collection

portfolio

publications

Google’s Quantum Supremacy Claim: Data, Documentation, and Discussion
With: Gil Kalai, Yosef Rinott, and Tomer Shoham
Preprint
arXiv
Questions and Concerns About Google’s Quantum Supremacy Claim
With: Gil Kalai, Yosef Rinott, and Tomer Shoham
Preprint
arXiv
Random Circuit Sampling: Fourier Expansion and Statistics
With: Gil Kalai, Yosef Rinott, and Tomer Shoham
Preprint
arXiv
Further Statistical Study of NISQ Experiments
With: Gil Kalai, Tomer Shoham, and Carsten Voelkmann
Preprint
arXiv
Maternal exposure to particulate matter early in pregnancy and congenital anomalies in offspring: Analysis of concentration-response relationships in a population-based cohort with follow-up throughout childhood
With: Ronit Nirel, Tomer Shoham, Ran Rotem, Wiessam Abu Ahmad, Gideon Koren, Itai Kloog, Rachel Golan, and Hagai Levine
Accepted to Science of The Total Environment , 2023
Paper
Statistical aspects of the quantum supremacy demonstration
With: Yosef Rinott, Tomer Shoham, and Gil Kalai
Accepted to Statistical Science , 2022
Paper
Asking the proper question: Adjusting queries to statistical procedures under differential privacy
With: Tomer Shoham, and Yosef Rinott
Accepted to International Conference on Privacy in Statistical Databases , 2022
Paper
Black-Box Differentially Private Nonparametric Confidence Intervals Under Minimal Assumptions
With: Tomer Shoham, Moshe Shenfeld, Noa Velner-Harris, and Katrina Ligett
Preprint
arXiv
Differentially private ratio statistics
With: Tomer Shoham, and Katrina Ligett
Preprint
arXiv

talks

teaching

67518 - Applied Data Privacy

Graduate course

We developed a course on differential privacy, using applied exercises to teach the theoretical concepts. The course is designed for graduate students in computer science, focusing on practical applications of differential privacy in data analysis and machine learning.

67508 - Foundations of Data Privacy

Undergraduate and graduate course

We developed hands-on active-learning sessions to accompany a theoretical introductory course on differential privacy. The course is designed for undergraduate and graduate students in computer science.