Grand Challenges in Visual Analytic Systems
Wednesday, October 19
3:45-5:00PM CDT (UTC-5)
Room: Pinon
Organizers: Aoyu Wu (HKUST), Dazhen Deng (Zhejiang University)
Panelists: Min Chen (University of Oxford), Shixia Liu (Tsinghua University), Daniel Keim (University of Konstanz), Ross Maciejewski (Arizona State University), Silvia Miksch (TU Wien), Hendrick Strobelt (IBM Research AI)
In the past two decades, research in visual analytic (VA) systems has made tremendous progress, not just in terms of publications, but also in making successful applications in wide-ranging domains. Despite applause, we often hear several open and provocative questions from end users, developers, and peer researchers regarding the research value and rigor, such as: What can the visualization community learn from VA system research beyond solving ad-hoc domain problems? Does the field value too much on novel yet often complex visualizations? What are opportunities for more rigorous evaluation strategies? Is there a complete theory of visual analytics? This panel brings together six academic and/or industrial researchers with extensive experience in VA. The panelists will discuss grand challenges in visual analytic systems for making the research field more rigorous, valuable, and impactful. We anticipate this panel will function as a way of airing and calling for action on those challenges.
Merits and Limits of User Study Preregistration
Thursday, October 20
10:45AM-12:00PM CDT (UTC-5)
Room: Pinon
Organizers: Lonni Bensançon (Linköping University), Cody Dunne (Northeastern University), Mohammad Ghoniem (Luxembourg Institute of Science and Technology)
Panelists: Brian A. Nosek (Center for Open Science, Inc. & University of Virginia), Tamarinde Haven (Charité Universitätsmedizin Berlin), Miriah Meyer (Linköping University)
The replication crisis has spawned a revolution in scientific methods, aimed at increasing the transparency, robustness, and reliability of scientific outcomes. In particular, the practice of preregistering study designs has shown important advantages. Preregistration can help limit questionable research practices, as well as increase the success rate of study replications. Many fields have now adopted preregistration as a default expectation for published studies. Yet, visualization research has only sparsely relied on this practice due to concerns about its lack of adequacy for visualization research and methods. With this panel, we have several goals: (1) explain the concept of preregistration to a wide visualization audience, (2) refute common misconceptions about the preregistration process, (3) provide insights about the merits and limits of preregistration gleaned from various fields, and (4) address the suitability of preregistration for a variety of types of visualization research.
Is This (Panel) Good Enough for IEEE VIS?
Friday, October 21
9:00-10:15AM CDT (UTC-5)
Room: Pinon
Organizers: Robert S. Laramee (Nottingham University), Petra Isenberg (INRIA), Tobias Isenberg (INRIA)
Panelists: Cody Dunne (Northeastern University), Alexander Lex (University of Utah), Torsten Möller (University of Vienna), Alvitta Ottley (Washington University, St. Louis), Melanie Tory (Northeastern University)
“The academic review process is broken,” is a statement one often reads or hears. After getting the reviews back from the IEEE VIS conference, likely 75% (or so) of us agree. But is it really? The goal of this panel is to discuss the review process of the visualization community (broader than just IEEE VIS) and to brainstorm ways to improve upon it or to come to the conclusion that everything is fine.