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Graduate Seminar | Towards Enabling Understanding and Interpretability in Data Analysis

Towards Enabling Understanding and Interpretability in Data Analysis 

Dr. Rebecca Faust
Friday, November 4, 2022
2:30-3:45 PM
Torgersen 2150


Data analysis is used widely across domains, often for decision making tasks. However, one of the central problems in data analysis is the lack of interpretability and understandability of results. These methods rarely provide insight into the internal operations that led to the results and are often classified as “black box”.  My research focuses on employing visualization to illuminate behaviors in analysis methods and provide additional insights into the final results. In this talk, I will demonstrate my efforts along two avenues: 1) improving interpretability of dimensionality reduction plots, and 2) enabling understanding in general analysis methods. 



Rebecca Faust is a postdoctoral researcher and Computing Innovations Fellow in the Sanghani Center for AI and Data Analytics at Virginia Tech, working with Dr. Chris North.  She received her Ph.D. from the University of Arizona, where she was advised by Dr. Carlos Scheidegger.  Her research interests lie at the intersection of data visualization and data analysis, with an emphasis on improving explainability of analysis methods and understandability of analysis results.