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.

Accepted Workshops

The following workshops went through our submission/review process.

TopoInVis: Workshop on Topological Data Analysis and Visualization

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


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
Jürgen Bernard, University of Zurich
Michael Correll, Northeastern
Mai Elshehaly, City University of London
Kyle Hall, University of Calgary
Daniel Keefe, University of Minnesota
Mahsan Nourani, Northeastern


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


The VISxAI workshop is a meeting place for researchers interested in explaining machine learning models through visualization. We focus on explainable 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.

NLVIZ Workshop: Exploring Research Opportunities for Natural Language, Text, and Data Visualization

Vidya Setlur, Tableau Research
Arjun Srinivasan, Databricks


Natural language processing (NLP) has evolved as a promising field for visual analysis and communication. The applications of NLP for supporting various aspects of the visual analysis workflow include helping readers take away key information from charts or dashboards, supporting interaction modalities that help people naturally ``ask” questions of their data, generating data summaries and insight reports, and exploring ways to enrich the semantics of data, among others. With data-driven communication being more important than ever, how do we treat text and language as first-class citizens in helping people see and understand data? How do we couple language and charts to make the data more accessible to a variety of audiences with different needs, capabilities, and skills? As the field of NLP matures, computers now have an increased capability of interpreting language and engaging in conversations with people. But can NLP techniques and interactive visualizations work in concert to support an analytical conversation? As the platforms and channels for exploring data go beyond the desktop to chat interfaces, augmented and virtual reality environments, mobile, and large displays, how do we better understand user intent, modalities, and context to make these interactions more delightful and meaningful? Addressing these questions calls for research at the intersection of human-computer interaction, information visualization, and NLP, three fields with natural synergies but rather infrequent meetings. This workshop will assemble an interdisciplinary community that promotes collaboration across these fields, explores research opportunities and challenges, and continues to establish an agenda for NLP research specifically for data visualization.

Progressive Data Analysis and Visualization (PDAV) Workshop

Alex Ulmer, Fraunhofer IGD
Jaemin Jo, Sungkyunkwan University
Michael Sedlmair, University of Stuttgart
Jean-Daniel Fekete, Université Paris-Saclay


The increasing amount of data is a long-standing challenge for data analysis systems. Although building these interactive systems has been a central focus of the visualization community, when applied to large-scale data as now used in areas such as machine learning, most current visualization systems suffer from long, unmanaged computation delays between user interactions and system response, rendering them unscalable to interactive data analysis. The critical challenge we face here is to make a system’s latency manageable, ultimately ensuring it remains below the golden limits of human latency (0.1 s, 1 s, or 10 s depending on the context) regardless of the amount of input data. To tackle this issue, the idea of progressive data analysis (PDA) is becoming increasingly important and might eventually even lead to a novel paradigm for interactive data analysis at scale. In previous computation paradigms, results either become available to the user once it is fully completed (i.e., sequential computation) or the result is incrementally updated but without a bounded pace (i.e., online computation). In contrast, progressive data analysis aims to deliver results to the user with increasing accuracy at a controlled pace, usually specified as a time limit between the results. The nature of PDA inherently leads to the need for interdisciplinary collaboration, as every component in the data analysis pipeline, from data management to visualization, needs to be progressive to make the entire system time-bounded. Since its introduction, progressive data analysis has quickly gained attention from the data science community and has already spawned different research contributions. These contributions can be roughly categorized into two themes: 1) computational components for PDA, e.g., progressive algorithms and data structures, and 2) human and visualization aspects of PDA, e.g., Progressive Visual Analytics (PVA). Examples of such contributions include a Dagstuhl report, human/user perspective, cognitive biases, progressive dimensionality algorithms, such as progressive t-SNE, and proof-of-concepts systems. While these are good starting points, many critical questions remain unanswered; for example, with PDA, humans can immediately see intermediate results, but how long should they wait before considering such results as good enough to make a decision? How can we measure the quality and accuracy of intermediate results when the final answer is unknown? What needs to be visualized in PVA, and how can we visualize it in a stable, faithful, and timely manner? How should people interact with progressive components, and what kinds of cognitive characteristics should be considered? Finally, how do the specific characteristics of PDA systems influence the way we evaluate them? The workshop aims to raise the visibility of progress made in the field of progressive data analysis and visualization, introduce this emerging topic to the VIS community, and bring together researchers and practitioners working or interested in PDAV.

Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks

Tushar M. Athawale, Oak Ridge National Laboratory
Chris R. Johnson, University of Utah
Kristi Potter, National Renewable Energy Laboratory
Paul Rosen, University of Utah
David Pugmire, Oak Ridge National Laboratory


Uncertainty visualization has become an increasingly important topic given the ubiquity of noise in data and computational processes. Although the research in uncertainty visualization has steadily progressed over the past few years, this critical branch of visualization is still in its infancy given many difficult challenges (e.g., computation, rendering, perception and decision-making) relevant to communication and understanding of uncertainty. One important step to address these challenges is to provide a venue that attracts a wide range of experts across many disciplines. A venue that allows experts in visualization, applications, applied math, perception, and cognition to publish and discuss effective ways to convey and understand uncertainty is an important step in advancing this critical area of research. The goal of the workshop is to bring together this multi-disciplinary group to enlighten the visualization community in the following four areas: (1) use cases in diverse application domains that can benefit from visualization of uncertainty (2) theory, techniques, and state-of-the-art software for uncertainty visualization (3) Methods/workflows that enable robust decisions under uncertainty (4) development of a future roadmap of uncertainty visualization research goals.

Workshop on Data Storytelling in an Era of Generative AI

Xingyu Lan, Fudan University
Leni Yang, The Hong Kong University of Science and Technology
Zezhong Wang, Canada Simon Fraser University
Yun Wang, China Microsoft Research Asia
Danqing Shi, Finland Aalto University
Sheelagh Carpendale, Canada Simon Fraser University


Storytelling is an ancient and precious human ability that has been rejuvenated in the digital age. Over the last decade, there has been a notable surge in the recognition and application of data storytelling, both in academia and industry. Recently, the rapid development of generative AI has brought new opportunities and challenges to this field, sparking numerous new questions. These questions may not necessarily be quickly transformed into papers, but we believe it is necessary to promptly discuss them to help the community better clarify important issues and research agendas for the future. We thus invite you to join our workshop (Gen4DS) to discuss questions such as: How can generative AI facilitate the creation of data stories? How might generative AI alter the workflow of data storytellers? What are the pitfalls and risks of incorporating AI in storytelling? We have designed both paper presentations and interactive activities (including hands-on creation, group discussion pods, and debates on controversial issues) for the workshop. We hope that participants will learn about the latest advances and pioneering work in data storytelling, engage in critical conversations with each other, and have an enjoyable, unforgettable, and meaningful experience at the event.

1st Workshop on Accessible Data Visualization

Naimul Hoque, University of Maryland
Pramod Chundury, University of Maryland
Frank Elavsky, Carnegie Mellon University
Lucas Nadolskis, University of California
Keke Wu, University of North Carolina at Chapel Hill
Laura South, Northeastern University
Brianna L Wimer, University of Notre Dame
Dominik Moritz, Carnegie Mellon University
Danielle Albers Szafir, University of North Carolina-Chapel Hill
Jonathan Lazar, University of Maryland
Niklas Elmqvist, Aarhus University


The ubiquity of data visualization across various domains—from data science and machine learning to business intelligence, medical science, and education—demonstrates its critical role in conveying complex information. However, it is now well-known that visualizations may create inequitable access to information for people with different disabilities (e.g., vision, motor, or cognitive disabilities). In response, the accessibility and visualization fields have sought to increase the accessibility of data visualizations for different populations. Examples of research in this area include interviews and observational studies with users with disabilities to understand accessibility issues with visualization, proposing theoretical frameworks, and designing technical solutions such as generating alt text, sonification, or physical artifacts. Despite these efforts, many visualization interfaces and tools remain inaccessible to users with various forms of disabilities. Building on the growing interest and open challenges at this intersection, the in-person Accessible Data Visualization (AccessViz) workshop aims to gather researchers, practitioners, and representatives from disability community at a common platform where we can formulate a community, share innovative discoveries, and envision the future of accessible data visualization research at IEEE VIS. The outcome of this workshop will inspire new researchers and lay down the path for promoting this line of research at IEEE VIS in the future.

EnergyVis 2024: 4th Workshop on Energy Data Visualization

Kenny Gruchalla, National Renewable Energy Lab
Anjana Arunkumar, Arizona State University
Arnaud Prouzeau, Université de Bordeaux
Lyn Bartram, Simon Fraser University
Sarah Goodwin, Monash University


The energy sector is witnessing significant technological progress, primarily driven by the growth of renewable energy, distributed energy resources, and smart grid technologies. This rapid evolution is generating increasingly large, complex data that present substantial challenges for energy systems planning and operations. For example, previously, distribution feeders had only a handful of sensors and controllable devices; now, with these new technologies, thousands or even tens of thousands of such devices are possible. As a result, energy system models have grown exponentially in planning scenarios from just hundreds of components to millions. Moreover, the adoption of distributed solar generation and grid-aware devices such as smart thermostats and electric vehicles are expanding the breadth of stakeholders, including consumers, engineers, regulators, urban planners, and policymakers, who are trying to understand these energy systems. These new energy systems are generating vast amounts of complex data, which require visualization techniques capable of handling the sheer scale and multifaceted complexity of the information. Unfortunately, much of the visualization supporting these changes is outdated, with simple one-line diagrams and contour plots being over-extended by the data they are being applied to. More research is needed to develop new and innovative visualization methods that can handle the increasing complexity of energy systems and provide a diversity of stakeholders with the necessary insights to make informed decisions about the future of energy. The EnergyVis 2024 workshop aims to bring together scientists, researchers, and practitioners from the energy and visualization domains to critically assess and discuss energy data visualization in the context of the evolving energy sector.

Visualization for Climate Action and Sustainability

Benjamin Bach, University of Edinburgh
Fanny Chevalier, University of Toronto
Helen-Nicole Kostis, USRA/GESTAR NASA/GSFC, United States
Mark SubbaRao, NASA Goddard Space Flight Center
Yvonne Jansen, Univ. Bordeaux
Robert Soden, University of Toronto


This first workshop on visualization for climate action and sustainability aims to explore and consolidate the role of data visualization in accelerating action towards addressing the current environmental crisis. Given the urgency and impact of the environmental crisis, we ask how our skills, research methods, and innovations can help by empowering people and organizations. We believe visualization holds an enormous power to aid understanding, decision making, communication, discussion, participation, education, and exploration of complex topics around climate action and sustainability. Hence, this workshop invites submissions and discussion around these topics with the goal of establishing a visible and actionable link between these fields and their respective stakeholders. The workshop solicits work-in-progress and research papers as well as pictorials and interactive demos from the whole range of visualization research (dashboards, interactive spaces, scientific visualization, storytelling, visual analytics, explainability etc.), within the context of environmentalism (climate science, sustainability, energy, circular economy, biodiversity, etc.) and across a range of scenarios from public awareness and understanding, visual analysis, expert decision making, science communication, personal decision making etc. Af- ter presentations of submissions, the workshop will feature dedicated discussion groups around data driven interactive experiences for the public, and tools for personal and professional decision making.

VISions of the Future: Workshop on Sustainable Practices within Visualization and Physicalisation

Georgia Panagiotidou, King’s College London
Andrew M McNutt, University of Washington
Sarah Hayes, Munster Technological University
Luiz Morais, Universidade Federal de Pernambuco
Derya Akbaba, Linköping University
Tatiana Losev, Simon Fraser University


Inspired by HCI, environmental sciences, and intersectional feminism, we see the growing need to discuss sustainability for visualization. With this workshop, we seek to establish a forum for considerations about the sustainability of visualization and physicalization practices. We view sustainability in broad terms - referring to the long-term viability of and effects on our environment, our infrastructure, our research practices, and our community. Rather than being disparate threads, we see these perspectives as intertwined with long-term, big-picture thinking related to a decolonial agenda that is often missing in day-to-day research practices. This half-day workshop will offer a venue for work on sustainability in VIS broadly, which - among other topics - might include reflections on internal practices, discussions of the potential of visualization as a discipline to support sustainability efforts, or opportunities to learn from sustainability experts outside the VIS community. Through this work, we seek to foster a community of interest, to characterize what sustainability goals for our field might look like, and to build a vision for how VIS might endure for the coming decades.

First-Person Visualizations for Outdoor Physical Activities: Challenges and Opportunities

Charles Perin, University of Victoria
Tica Lin, Harvard University
Lijie Yao, Université Paris-Saclay
Yalong Yang, Georgia Institute of Technology
Maxime Cordeil, The University of Queensland
Wesley Willett, University of Calgary


This half-day workshop will gather researchers and practitioners interested in first-person visualizations for outdoor physical activities. Given the unexplored nature of the topic, the goal of this first workshop is to collect speculative designs informed by experience and expertise. Participants will mainly submit fictional case studies in the forms of illustrated submissions along with a statement that demonstrates their knowledge/expertise of the case study. Our goal is to build on these speculative designs to i) explore the space of first-person visualizations for outdoor physical activities and ii) derive a research agenda for the visualization community. We envision this output to take the form of a publication of which workshop participants will be invited to become co-authors.

EduVis: 2nd IEEE VIS Workshop on Visualization Education, Literacy, and Activities

Fateme Rajabiyazdi, Carleton University
Mandy Keck, University of Applied Sciences Upper Austria
Christina Stoiber, St. Pölten University of Applied Sciences
Jonathan Roberts, Bangor University
Hari Subramonyam, Stanford University
Lily Ge, Northwestern University
Magdalena Boucher, St. Pölten University of Applied Sciences
Benjamin Bach, University of Edinburgh
Lonni Besançon, Linkoping University
Mathis Brossier, Linkoping University
Anders Ynnerman, Linkoping University
Konrad Schonborn, Linkoping University
Alon Friedman, University of South Florida
Bo Pei, University of South Florida
Ly Dinh, University of South Florida
Kevin Hawley, University of South Florida
Julia Woodward, University of South Florida
Paul Rosen, University of Utah
Md Dilshadur Rahman, University of Utah
Yan Chen, University of Virginia Tech


This is the second IEEE VIS EduVis Workshop on Visualization Education, Literacy, and Activities. This workshop aims to become a forum to share and discuss advances, challenges, and methods at the intersection of visualization and education. The workshop addresses an interdisciplinary audience from and beyond visualization, education, learning analytics, science communication, psychology, or people from adjacent fields such as data science, AI, and HCI. It will include presentations of research papers and working group discussions.