Eugene Wu develops systems and algorithms for modern interactive data analysis.

His research focuses on the full interactive data analysis stack: from data cleaning and preparation, to scalable systems for interactive exploration interfaces, to automatic interface generation, to explanation tools that help explain anomalies encountered during data analysis. His current project is the Data Visualization Management System, which integrates concepts from database research, such as declarative languages, query optimization, and lineage, with interactive visualizations, making it easier to design, architecture, build, and scale rich visual data exploration systems.

Wu’s research spans the areas of core database optimization, stream processing systems, crowd sourcing, data visualization, data cleaning, and HCI. His work includes SASE, one of the first high performance complex event processing systems for high throughput data streams; Scorpion, which introduced a novel analysis feedback system that explains anomalies that analysts find in data visualizations; ActiveClean, the first interactive data cleaning algorithm designed for data science; and Precision Interfaces, the first large-scale automatic interface generation system. His current work in data visualization management systems draws connections between data visualizations and data processing systems and unifies them under a single system abstraction. The interdisciplinary nature of his research leads Wu to work closely with researchers in information visualization, perception, theory, and machine learning.

Wu received a BS in electrical engineering and computer science from UC Berkeley in 2006, a PhD in electrical engineering and computer science from MIT in 2015, and was a Postdoctoral Fellow at UC Berkeley in 2015.