Interactive Data Analysis: Visualization and Beyond

Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, this talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools.

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 Jeff and his collaborators (Vega, D3.js, Protovis, Prefuse) are used by researchers, companies, and thousands of data enthusiasts around the world. Jeff’s research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, UIST, CSCW; IEEE InfoVis, VAST, EuroVis). Other honors include MIT Technology Review’s TR35 (2009), a Sloan Fellowship (2012), and the ACM Grace Murray Hopper Award (2016). Jeff holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then betrayed to join the Stanford faculty (2009–2013). He is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.