I am a Sr. Principal Researcher in the Human-Computer Interaction and EPIC research groups at Microsoft Research, where I work in Human-Computer Interaction with an emphasis on the design, development, and evaluation of novel visualization and interaction techniques. I explore innovative ways for people to create visualizations, interact with data, and share data-driven stories. I have also been focusing on helping people collect & explore the data about themselves and share meaningful insights with others by leveraging visualizations.
I currently serve as IEEE Visualization Executive Committee (VEC) and Steering Committee for ACM ISS, as well as Overall Papers Co-Chair for IEEE VIS 2021 and Subcommittee Co-Chair for ACM CHI 2021 for the Visualization Subcommittee. I served as General Co-Chair for ISS 2019 and IEEE PacificVis 2017, Associate Editor for IEEE TVCG, and Papers Co-Chair for IEEE InfoVis 2015 & 2016, and IEEE PacificVis 2018. I am a member of the IEEE Visualization Academy. I earned my MS and PhD in Computer Science from University of Maryland at College Park in 2002 and 2006, respectively.
Reaching Broader Audiences with Data Visualization
The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this paper, we discuss four research topics—personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization—that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible.
Broadening Intellectual Diversity in Visualization Research Papers
Promoting a wider range of contribution types can facilitate healthy growth of the visualization community, while increasing the intellectual diversity of visualization research papers. In this paper, we discuss the importance of contribution types and summarize contribution types that can be meaningful in visualization research. We also propose several concrete next steps we can and should take to ensure a successful launch of the contribution types.
Expanding Research Methods for a Realistic Understanding of Personal Visualization
We call for attention to realism in empirical investigations of personal visualizations. “Realism of the situation or context within which the evidence is gathered, in relation to the contexts to which you want your evidence to apply” is one of three desirable criteria for study design. Research methods commonly used in visualization often focus on the other two criteria: generalizability (maximizing the range of people to whom the results are applicable) and precision (sufficiently controlling external variables so as to isolate a specific effect and supply a degree of confidence). In an ideal world, we could design studies that maximize all criteria. In practice, however, trade-offs have to be made.
More than Telling a Story: A Closer Look at the Process of Transforming Data into Visually Shared Stories
We aim to facilitate better structured discussions around compelling techniques for storytelling with data visualization by drawing a line between a visual data story and a general data visualization and narrowing the scope of what a visual data story is. Considering the entire process of transforming data into visually shared stories along with this more focused definition, we believe it is possible to widen the scope of research around visual data stories. By pursuing these new avenues of research in visual data storytelling process, visualizations can enable more effective storytelling with data.
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