Data analysis is a complex process with frequent shifts among data formats and models, and among textual and graphical media. We are investigating how to better support the life cycle of analysis by identifying critical bottlenecks and developing new methods at the intersection of data visualization, machine learning, and computer systems. Can we empower users to transform and clean data without programming? How can we support more expressive and effective visualization tools? How might we enable domain experts to guide machine learning methods to produce effective models? This talk will present selected projects that attempt to address these challenges and introduce new tools for interactive visual analysis.

Jeffrey Heer is an Associate Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by his lab (D3.js, Vega, Protovis, Prefuse) are used by researchers, companies and thousands of data enthusiasts around the world. His group's research papers have received awards at the premier venues in Human-Computer Interaction and Information Visualization (ACM CHI, ACM UIST, IEEE InfoVis, IEEE VAST, EuroVis). Other awards include MIT Technology Review's TR35 (2009), a Sloan Foundation Research Fellowship (2012), and a Moore Foundation Data-Driven Discovery Investigator award (2014). Jeff holds BS, MS and PhD degrees in Computer Science from UC Berkeley (whom he then betrayed to go teach at Stanford from 2009 to 2013). Jeff is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.