1:20pm Room B - Strategies in Visualizing Networks
Network data models entities and the relationships between them. Examples include social networks, biological pathways, and computational dependency graphs. Visualizing networks can provide context to the data, verify or refute assumptions about structure, or help communicate processes. However, network visualization can quickly become cluttered and difficult to interpret. In this talk, I will go over tools and strategies for visualizing networks, with examples from my research in debugging and optimizing computer programs.
Dr. Kate Isaacs
Department of Computer Science
University of Arizona
Kate Isaacs is an assistant professor in the computer science department at the University of Arizona. Her interests are in data visualization with a focus in application-driven problems and methodologies. Her work has led to new methods of representing complex computing processes for exploration and analysis of their behavior, with applications to high performance computing, distributed computing, data science, program analysis, and optimization. In 2019, she was awarded an NSF CAREER Grant for this work.