Speaker:Lin Yan

Title: Topology-Based Visualization Techniques for Scientific Data Exploration

Abstract: Topological data analysis (TDA) has been used to visualize, summarize, and understand complex data in science and engineering, ranging from climate and neuroscience to cosmology. However, data's ever-increasing complexity and size pose grand challenges to traditional methodologies and necessitate TDA to understand essential features, sensitivities, and uncertainties from science simulations, experiments, and observations.

This talk covers three topics addressing these challenges by enriching methodologies and tools of topology-based visualization for scientific data exploration. First, I will present a merge-tree-based comparative measure using labeled interleaving distances for scalar fields. Such merge tree comparison helps detect transitions, clusters, and periodicities for time-varying datasets; the metric makes it possible to derive the structural average of labeled merge trees for uncertainty visualization. Second, I will illustrate using merge trees to quantify the structural stability of vector field features to perturbations. Specifically, this framework can enhance feature tracking, selection, and comparison in climate reanalysis data for tropical cyclone (TC) tracking. Third, I will present my work on developing advanced data reduction techniques and software that preserve topological features in data for in situ and post hoc analysis and visualization at extreme scales.

Bio: Dr. Lin Yan is a postdoctoral fellow at the Environmental Science & Mathematics and Computer Science Division at Argonne National Laboratory. She received BS and MS in control science and engineering from Shanghai Jiao Tong University and her Ph.D. in computer science from the University of Utah. Her research interests include topological data analysis and visualization. Her recent work focuses on problems involving large and complex forms of data by combining topological, statistical data analysis, machine learning, and visualization techniques.