My research lies at the intersection of machine learning, statistics, information processing, and networked systems. I am particularly interested in developing data- and compute-efficient learning algorithms that operate in resource-constrained environments. Much of my work focuses on interactive learning—algorithms that not only learn from data but also decide what data to collect next. A recurring theme is leveraging structure—graphs, manifolds, or physical laws—to inform both inference and acquisition. Recent applications I have worked on include power grid monitoring, neuroscience, meta-science, circuit design, and epidemiological forecasting.