Shuman’s research interests focus on signal processing on graphs and stochastic control. He is currently investigating new tools, theory, and algorithms for processing high‐dimensional data on graphs, which arise in application areas such as energy, social, biological, and sensor networks, machine learning, medical imaging, and astrophysics. With an interdisciplinary background in economics, operations research, electrical engineering, and applied mathematics, he collaborates with researchers from numerous disciplines and strives to make connections between different fields, both in the classroom and in research. In his spare time, David enjoys playing sports, hiking, traveling, photography, and cooking.
