Teaching

Together with Tomer Shoham, I co-developed and taught the Applied Data Privacy course at the Hebrew university, a complementary 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. Finally, the students implement classical neural networks training algorithms, extract private details about the training data from the final model, and implement efficient privacy protection techniques.

I am currently transforming the course to an online format, which will be available in early 2026.

Service

For the past two years, with Katrina Ligett, I co-organized 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 committee in NeurIPS (2022-2025), ICML (2022-2024), TPDP (2025), ICLR (2026), and SatML (2026).