Eugene Wu

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.