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.
Blog And News
Doing a PhD in computer science (or any other field) is a challenging but often intellectually fulfilling experience. I certainly had a great time in grad school, and I’m sure that many other students also did.
There are several guides out there that discuss how to apply to grad school in CS, and they contain very useful advice. I want to contribute a complementary perspective here: I want to talk about what you should do, starting maybe in your second or third year of college, to increase your chances of getting into grad school.
I’m writing this because I sometimes get requests for letters of recommendation for grad school from students that take a class I’m teaching who I otherwise don’t know. The classes I teach are often rather large (~100 students) so that I unfortunately don’t have an opportunity to get to know many students that well....
Image by Clara Nellist, used with permission.
I’ve been thinking a lot about how to structure VIS in my role as a member of the reVISe committee. Last week at VIS, the committee got a few questions regarding changes to the review process. reVISe explicitly did not tackle reviewing beyond what was necessary for the unification of the three conferences. One step after another.
I do have some personal opinions about the review process and what we should (not) change, which I wrote up here. Note that this does NOT reflect the reVISe committee’s opinion – we didn’t even discuss most of these issues.
So, here we go:
1. We should be doing double-blind reviews.
Research in our lab is often conducted through design studies; applied visualization research to address real-world problems. In recent work, we experimented with methods to conduct design studies from an interpretivist perspective. In this blog post, we advocate for interpretivist design studies and give recommendations for conducting design studies through an interpretivist lens. This perspective can lead to more diverse contributions, more rigor, and learning opportunities.
But first – what is an interpretivist design study? What do we mean by interpretivist? Much of the established methods for conducting scientific research is grounded in a positivist approach to inquiry, where there is a single reality that can be understood by observation. Interpretivism stands in contrast to positivism and holds that reality is subjective, socially constructed, and a composite of multiple perspectives. Through this lens, research is inherently shaped by the researcher, who brings their own subjective view of observed...
How did we get to that particular analysis result? That’s a question that’s easy to answer when we do data analysis with scripting languages, such as R and Python. In fact, computational notebooks such as Jupyter notebooks, Observable, or R Markdown have come remarkably far in fulfilling Knuth’s vision of literate programming – programming that emphasizes readability and understandability.
In contrast, if we use a visual analysis system – with its many advantages – to investigate a research question, we might arrive at a conclusion through a sequence of actions (view specifications, filters, brushes) but these sequences are typically lost after the analysis and can’t be easily reproduced and scrutinized. And while computational notebooks make it easy to add comments to justify analysis decisions, most visual analysis systems can’t capture an analyst’s thought process.
So can we achieve reproducibility in interactive web-based visualizations? How? These are questions we are looking at in...