Workshops

Pre-Approved Workshops

These workshops were pre-approved by the VIS Executive Committee. Please visit their individual websites for details on the topics and submission deadlines.

TopoInVis: Workshop on Topological Data Analysis and Visualization

Divya Banesh, Los Alamos National Laboratory
Guoning Chen, University of Houston

Contact: topoinvis@ieeevis.org

The IEEE VIS Workshop on Topological Data Analysis and Visualization aims at being an inclusive forum for the fast dissemination of the latest results in theory, algorithms, and applications of topological methods for the interactive and visual analysis of data. This workshop is a remodeling of the established TopoInVis workshop series, with the goal of being more diverse (in terms of applications) and inclusive (in terms of communities), with a clear will to open to other members of the visualization community potentially interested in topological methods, or experts in topological methods from other communities willing to experiment with interactive and visual applications.

BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization

Anastasia Bezerianos, Université Paris-Saclay
Kyle Hall, University of Calgary
Samuel Huron, Télécom Paris
Matthew Kay, Northwestern
Michael Correll, Northeastern

Contact: beliv.workshop@gmail.com

BELIV 2024 will be open to discussions on how we establish the validity and scope of knowledge acquired in our domain including, in particular, all forms of methods used to acquire this knowledge. This broad scope is meant to entice critical reflection on ways to assess different forms of value offered by visualization research and design. This includes discussions on novel research methods but also existing methods and tools such as statistics. We also invite meta-discussions on empirical research practices in our domain, for example on what level of rigor to require of our methods, how to choose methods and methodologies, and how to best communicate the results of empirical research. This broad umbrella encompasses the topics that BELIV has been known to focus on, but expands in ways that we believe are important as our research community grows and matures.

VISxAI: 7th Workshop on Visualization for AI Explainability

Angie Boggust, MIT CSAIL
Mennatallah El-Assady, ETH AI Center
Alex Bauerle, CMU
Fred Hohman, Apple
Hendrik Strobelt, IBM Research

Contact: orga@visxai.io

The VISxAI workshop is a meeting place for researchers interested in explaining machine learning models through visual- ization. We focus on explainables submissions that visually and interactively explain machine learning concepts, ranging in complexity from clustering methods to algorithmic biases. The explainables serve as educational resources that have an impact beyond the academic community. The workshop hosts keynote speakers that expose visualization researchers to state-of-the-art machine learning methods and explore the impact visualization can have on explainability. Interactive audience sessions encourage conversations on critical topics in explainability and build relationships between attendees with multidisciplinary backgrounds. By bringing visualization and machine learning researchers together, the VISxAI workshop expands the problem space of explainability to include both machine learning and visualization and spurs new collaborations.

Bio+Med+Vis Workshop

Nils Gehlenborg, Harvard Medical School
Barbora Kozlikova, Masaryk University

The goal of the Biological and Medical Visualization Workshop (Bio+Med+Vis Workshop) is to educate, inspire, and engage visualization researchers and students in current problems in biological & medical data visualization. The event will serve as a platform for presenting the participants with the current state and research challenges in BioMedVis, their impact on other disciplines (e.g., personalized medicine, art), and public outreach, and will enable the participants to actively contribute to the workshop by submitting their works on our announced biological and medical visualization challenges.