Faculty

Complex systems, such as living cells and the Internet, involve networks of interactions and associations between large numbers of individual components. Examples include interactions between proteins in biological cells, connections between users in communication or social networks, and signaling within the brain. Developing statistical techniques to model and analyze large-scale networked systems is a grand challenge for the 21st century. For instance, new mathematical methods will lead to better models of biological systems and thus improvements in disease prevention. Broadly speaking, Rob Nowak's research focuses on mathematical theory and methods for making inferences about high-dimensional and nonparametric models of complex networked systems. He believes that progress in this area of research will dramatically accelerate the pace of discovery in science and engineering. Complex systems, such as living cells and the Internet, involve networks of interactions and associations between large numbers of individual components. Examples include interactions between proteins in biological cells, connections between users in communication or social networks, and signaling within the brain. Developing statistical techniques to model and analyze large-scale networked systems is a grand challenge for the 21st century. For instance, new mathematical methods will lead to better models of biological systems and thus improvements in disease prevention. Broadly speaking, his research focuses on statistical theory and methods for making inferences about high-dimensional and nonparametric models of complex networked systems \cite{crouse98,kola04,Castro04networktomography,scott05,kola05,Scott06,haupt06,rui:08,singh09,sparsa,bajwa10,haupt11,nowak11}. Nowak believes that progress in this area of research will dramatically accelerate the pace of discovery in science and engineering. His background and expertise lie at the intersection of statistics, engineering, and computing, with a focus on signal processing and machine learning.