I research machine learning and statistics with a focus on public and urban policy. And when I get the chance, I'll hike, travel, study Talmud, and embroil myself in politics.
I hold a dual PhD in Machine Learning and Public Policy from Carnegie Mellon University where I was advised by Daniel Neill (now at NYU). My work was partially funded by an NSF Graduate Research Fellowship and an ARCS Scholars Fellowship.
My research focuses on developing scalable models for multidimensional prediction and causal inference. Specifically, I am interested in Bayesian nonparametrics, kernel methods, and deep learning. These methods are particularly suited for analyzing complex data arising from human behavior, while still providing clear and interpretable results.
Using these methods I study dynamics of disease, crime, and transportation. Ultimately, my research is oriented towards helping policy makers create more targeted and effective policy interventions. Through better analytics and clearer communication between researchers and policy makers I believe we can make government smarter, smaller, and more just.
I grew up in New York City and studied electrical engineering at Princeton, graduating in 2012. For three years in college I conducted research in Paul Prucnal’s Lightwave Communications Research Lab where I developed a love for the joy and travails of scientific research. Afterwards, I worked at MIT Lincoln Laboratory conducting research on AI, robotics, and cybersecurity.