Ph.D. Student

Research Interest: data mining, Bayesian statistics, visualization, machine learning, probabilistic methods in principal component analysis, clustering algorithms, applications of stochastic processes to climate data. She worked as a RA for the NSF EACLIPSE project.


Currently I am a RA for BaVA project: BaVA website. The project is funded by the National Science Foundation, Computer and Communications Foundations; #0937071. FODAVA website.

The goal of this research is to combine two areas, Visual Analytics and Bayesian Statistics. Currently, visualizations display inflexible deterministic transformations of data that inherently separate data visualization from visual synthesis. Analysts cannot manipulate displays to inject domain-specific knowledge to formally assess the merger of their expert judgment with the data. However, by changing the nature of the data transformation from deterministic to probabilistic Bayesian methods, manipulations to a display are possible to interpret quantitatively. Thus, a new visualization model is developed which offers editable representations to promote bidirectional flow between analyst and data. This approach enables analysts to quantify data uncertainty, formally include expert judgment into analyses, rapidly generate and test new hypotheses, and allows multi-source and multi-scale data to contribute to one data display. The developed software is applied and evaluated with analysts in multiple fields, including intelligence analysis.


Selected Projects:

demo of the BaVA UI (gene function dataset): Mixture PPCA.

idea of interactive GTM: text clustering.

a package to streamline cutoff determination in immunoassay: automatic reports.

Office: 403-Y Hutcheson Hall
E-mail: chaohan@vt.edu

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