About
We are a team of visualization researchers at the University of Utah. Our interests include the process of designing and developing visualizations, visualization for biology, visualization frameworks, and, more generally, visualization of big, heterogeneous, and complex datasets.
VDL is part of the Scientific Computing and Imaging Institute and the School of Computing.
Blog And News
Selections are a key interaction in data visualization. They are used for highlighting and as the starting point for subsequent actions, including filters, group assignments, etc. Capturing the intent – WHY were these items selected? – can be used to help users refine their selections and to keep a meaningful history of the analysis process. This post discusses our paper on techniques to capture such intents.
Seven VDL lab members are back in person at IEEE VIS this year! We especially look forward to co-host the Utah Party together with other SCI members on Tuesday night at Social Capital! Make sure to say hi, come to our talks and to the party.
Our activities are:
- Haihan Lin is presenting her paper Data Hunches: Incorporating Personal Knowledge into Visualizations on Wednesday Oct 19th, 2-3:15 PM CDT.
- Moataz Abdelaal, who is visiting from the University of Stuttgart, is presenting his paper Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World on Wednesday, Oct 19th, 3:45-5 PM CDT.
- Moataz is also presenting his paper Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations on Thursday, Oct 20th, 10:45-12 PM CDT.
- Alex Lex is on the panel for Is This (Panel) Good Enough for IEEE VIS? on Friday, Oct 9-10:15 AM CDT.
- Haihan and Kiran Gadhave are attending the Doctoral Colloquium.
- And finally, Kiran is helping out at VIS as a student volunteer!
Have you ever looked at data visualization and thought: that doesn't look right. Maybe you knew more about the data than is actually contained in the dataset. Did you then remember that hunch throughout your data analysis process, impacting your judgment and interpretation of the data? That thought, whether you were aware of it or not, possibly impacted your interpretation. Especially if that hunch is based on knowledge you have about the data, it would be useful to externalize that hunch, so others can learn about it and also consider it in their analysis. However, current visualization methods do not support this. In this blog post, we dive into how we came up with data hunches to describe personal knowledge brought to data analysis. We explore methods and designs to record and communicate data hunches through visualizations explicitly.
In this blog post, we want to tell two stories: First, we want to explore how visualization can be used to select cancer treatments. And second, we want to tell a more technical story of how you can use exemplars to deal with large datasets that you can’t easily summarize.
