I am a PhD Candidate in Computer Engineering at Bar-Ilan University, advised by Prof. Or Sheffet, where my research focuses on Differential Privacy, high-dimensional statistics, and the design of efficient and private algorithms. My work bridges rigorous mathematical theory with practical privacy-preserving data analysis and has been presented at top-tier conferences including NeurIPS 2022, NeurIPS 2025, and COPA 2025.

Alongside my academic research, I served as Head of Department in the J6 & Cyber Defence Directorate of the Israel Defense Forces (IDF), where I led three multidisciplinary teams in RF engineering, full-stack development, and DevOps. My role involved guiding R&D efforts in advanced communication systems, algorithmic innovation, and large-scale software infrastructure.

I am passionate about developing theoretical foundations that enable trustworthy machine learning and privacy-aware computation. My broader interests include probabilistic learning theory, optimization, and applied cryptography.

I also enjoy teaching—serving as an Assistant Lecturer at Bar-Ilan University, where I teach courses in Computer Networks, Software Engineering, and Operating Systems.

Beyond research, I’m an avid soccer player, acrobatics enthusiast, and pianist.

Research Interests

My research lies at the intersection of Differential Privacy, Machine Learning Theory, High-Dimensional Statistics, and Geometric Optimization. I am particularly interested in developing efficient, private, and interpretable algorithms for modern data analysis — bridging the gap between theoretical guarantees and real-world performance.

As I approach the completion of my PhD in Computer Engineering (expected Fall 2026), I am seeking post-doctoral opportunities that continue this line of research, focusing on privacy-preserving learning, statistical generalization under uncertainty, and robust algorithmic design.

In parallel, I am also open to research-driven internships in the industry, where I can apply my academic expertise to large-scale, applied settings involving privacy, trustworthy AI, and algorithmic optimization.