Dr. Wang graduated Summa Cum Laude from the University of Bridgeport in 2003 with a bachelor's degree in Computer Science and Mathematics, and a minor in Psychology. She received her Ph.D. in Computer Science from Duke University in 2010. There, she also earned a Certificate in Computational Biology and Bioinformatics. During her last year at Duke, she was also a visiting researcher at the Institute of Science and Technology, Austria. She was a postdoctoral fellow from 2010 to 2011, and a research computer scientist from 2011 to 2016, both at the Scientific Computing and Imaging (SCI) Institute, University of Utah.
Dr. Wang is currently an Assistant Professor in the School of Computing and a faculty member in the SCI Institute.
Dr Wang's research expertise lies in the theoretical, algorithmic, and application aspects of data analysis and data visualization, with a focus on topological techniques. In particular, her research leverages topological data analysis, which provides a strong theoretical basis for transforming large, complex data into compact, structure-highlighting representations. Such representations connect naturally with and provide infrastructures for data visualization, and inspire the rethinking of interactive data exploration to facilitate analytical reasoning. Some of her current research activities draw inspirations from topology, geometry and machine learning, in studying vector fields, high-dimensional point clouds, networks and multivariate ensembles.
Dr. Wang's research interests span both data analysis and data visualization, including:
- scientific visualization
- information visualization
- topological data analysis
- computational topology
- computational geometry
- machine learning
- data mining