Eugene Wu is broadly interested in technologies that help users play with their data. His goal is for users at all technical levels to effectively and quickly make sense of their information. He is interested in solutions that ultimately improve the interface between users and data, and techniques borrows from fields such as data management, systems, crowd sourcing, visualization, and HCI.
Current Research Areas
A Data Visualization Management System (DVMS) integrates visualizations and databases, by compiling a declarative visualization language into an end-to-end relational operator pipeline that renders the visualization and is amenable to database-style optimizations. Thus the DVMS can be both expressive via the visualization language, and performant by leveraging traditional and visualization-specific optimizations to scale interactive visualizations to massive datasets.
Visualizations are excellent for exposing surprising patterns and outliers in data, however existing tools have no way to help explain those patterns and outliers. We are exploring systems to generate sensible explanations for outliers in analytics visualizations.
Analysts report spending upwards of 80% of their time on problems in data cleaning including extraction, formatting, handling missing values, and entity resolution. The SampleClean project explores scalable techniques for data cleaning, crowd sourced data cleaning, and statistical inference on dirty data.