Are you considering grad school? Here are a few steps you can take to maximize your chances. The bad news is that it’s best to start planning and working towards getting into grad school early: I recommend you take first steps some time at the end of your sophomore year in college. The good news is that the process is...

The review process at IEEE VIS hasn’t changed in the last decade. Here I lay out some suggestions to make the process more equitable, open, and to possibly raise the quality of reviews.

Much of the current conducting and reporting on design studies leaves little room for contributions outside of the end tool or technique. An interpretivist approach embraces the messy, subjective nature of design studies and emphasizes the ways in which we can conduct research of this nature with rigor. In this post, we advocate for interpretivist design studies and give three...

Tracking a history of actions of an interactive visual analysis session (i.e., its provenance) has benefits ranging from simple undo/redo, to enabling reproducibility, to making post-hoc analysis of user sessions possible. However, there are no established provenance tracking libraries that visualization developers can use with their web-based tools. This blog post introduces Trrack – a web-based library designed for easy...

Crowdsourcing is a popular method for conducting quantitative studies, yet up to now, the types of studies that were run on crowdsourcing platforms is narrow. In this blog post, we describe a method to evaluate complex visualization techniques in a crowdsourced setting, which we developed for a 2020 CHI paper on evaluating multivariate network visualization techniques.

tldr: As states are navigating the influx of COVID data, they are using dashboards to communicate with their constituents. Although many states are doing a good job at collecting and sharing data in open data portals, dashboard quality varies a lot across states, and some have considerable room for improvement.

tldr: Using dashboards comes with risks: they leave out critical context by over-simplifying and hence give false certainty. A more nuanced approach including interpreation by experts, and showing multiple perspectives is needed when visualizing data for something as complex as the COVID-19 pandemic.

tldr: We're starting a blog to communicate about our research and share our thoughts on data visualization!