About

I am a Statistics PhD candidate at Tel Aviv University, advised by Prof. Daniel Nevo. My work sits at the intersection of causal inference, network science, and Bayesian inference. Prior to that, I completed my Master's in Statistics at the Hebrew University, where I worked on high-dimensional survival analysis models with Prof. David Zucker.

My research develops methods for estimating causal effects when treatments spill over through a network of connected units while the network itself is uncertain. This setting is called "network interference" and is ubiquitous in many domains, including social platforms, marketplaces, economic and social interactions, marketing, and epidemiology. My work starts from the simple observation that in many of these settings, accurately measuring networks of social interactions is formidably difficult. I study the consequences of such network misspecification for causal inference. My research encompasses several approaches and methods to address this challenge.

Alongside the methodological work, I provide statistical consulting for the IDF's Medical Corps research institute on multiple research projects involving electronic health records of over 500k patients. I also have several years of experience teaching Probability and Statistics at Tel Aviv and Hebrew Universities.