Teaching

Together with Tomer Shoham, I co-developed and taught the Applied data privacy course at the Hebrew university, a complementry course to the Foundations of Data Privacy course taught by Katrina Ligett.

Using a learning-by-doing approach, this course uses empirical evaluations and interactive simulations to provide an intuitive understanding of theoretical and practical concepts. The in-class coding tasks and homework assignments cover a series of de-anonymization attacks against seemingly private algorithms and implementation of provably privacy-preserving techniques. At the final stage of the course the students implement classical neural networks training algorithms, extract private details about the training datafrom the final model, and implement efficient privacy protection techniques.

I am currently transforming the course to an online format, which will be available at the beginning of 2026.

Community service

In the last two years, I co-organized with Katrina Ligett The Israeli Privacy and Fairness Workshop, an annual gathering that brings together researchers, students, and practitioners from Israel’s vibrant academic community working at the intersection of privacy and fairness in machine learning and data science.

Additionally, I have served as part of the program committeein NeurIPS (2022, 2023, 2024, 2025), ICML (2022, 2023, 2024), and TPTP (2025).