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, or contact the organizers directly if no website is available yet.

Accepted Workshops

These workshops went through our submission/review process. Please visit their individual websites for details on the topics and submission deadlines, or contact the organizers directly if no website is available yet.

VAHC 2021: 12th workshop on Visual Analytics in Healthcare

Jürgen Bernard, University of Zurich
Annie T. Chen, University of Washington
Danny T.Y. Wu, University of Cincinnati


Visual Analytics in Healthcare is the premier research event exploring the application of data visualization and visual analytics to biomedicine. VAHC 2021 will bring together medical experts, leading scientists, and visionaries to discuss opportunities and challenges in using visual analytics techniques to help patients, clinicians, public health researchers, and others leverage the power of complex health datasets.

VIS4DH: 6th Workshop on Visualization for the Digital Humanities

Houda Lamqaddamm, KU Leuven
Florian Windhager, Danube University Krems


The VIS4DH workshop brings together researchers and practitioners from the fields of visualization and the humanities to discuss new research directions at the intersection of visualization and (digital) humanities research.

VISxAI: 4th Workshop on Visualization for AI Explainability

Adam Perer, Carnegie Mellon University
Fred Hohman, Apple
Hendrik Strobelt, MIT-IBM Watson AI Lab
Mennatallah El-Assady, University of Konstanz


The role of visualization in artificial intelligence (AI) gained significant attention in recent years. With the growing complexity of AI models, the critical need for understanding their inner-workings has increased. Visualization is potentially a powerful technique to fill such a critical need. The goal of this workshop is to initiate a call for “explainables” / “explorables” that explain how AI techniques work using visualization. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.

Exploring Opportunities and Challenges for Natural Language Techniques to Support Visual Analysis Tasks

Vidya Setlur, Tableau Research
Arjun Srinivasan, Tableau Research


Natural language processing (NLP) has evolved as a promising field for helping users in their exploration and interaction with data for visual analysis tasks. The applications of NLP for supporting various aspects of the visual analysis workflow include: employing text processing methods for data preparation, supporting interaction modalities to help people naturally “ask” questions of their data, and generating captions and textual summaries to help readers take away key information from the chart or dashboard, among others. As the field of NLP matures, computers now have an increased capability of interpreting natural language and engaging in conversations with people. But, can NLP techniques and interactive visualizations work in concert to support an analytical conversation? If so, how? How can the increasing number of domain-specific corpora and their semantics be better utilized for supporting smarter data transformations and visualization defaults? How can we develop models to effectively summarize visualizations and highlight key data findings? Can text and charts be interactively linked to support interpretation and data-driven communication? Addressing these questions calls for research at the intersection of human-computer interaction, information visualization, and NLP, three fields with natural connections (pun intended) but rather infrequent meetings.This workshop will assemble an interdisciplinary community that promotes collaboration across these fields, explore research opportunities and challenges, and establish an agenda for NLP research specifically for data visualization.

Workshop on Audio-Visual Analytics – Towards a Research Agenda for Integrating Sonification and Visualization

Wolfgang Aigner, St. Poelten University of Applied Sciences
Kajetan Enge, St. Poelten University of Applied Sciences
Michael Iber, St. Poelten University of Applied Sciences
Alexander Rind, St. Poelten University of Applied Sciences
Niklas Elmqvist, University of Maryland, College Park
Robert Höldrich, University of Music and Performing Arts Graz
Niklas Rönnberg, Linköping University
Bruce N. Walker, Georgia Institute of Technology


This workshop aims to build a community of researchers from both the visualization and the sonification communities and to work towards a common language, find research gaps, and identify directions for research on audio-visual data analysis idioms. We scheduled the larger part of the day for group and plenary discussions. Therefore, there will be no call for presenters and it is open to all IEEE VIS 2021 attendees. We expect the workshop to result in an outline for a research agenda to combine visualization and sonification.

TREX: Workshop on TRust and EXpertise in Visual Analytics

Mahsan Nourani, University of Florida
Eric Ragan, University of Florida
Emily Wall, Northwestern University
Alireza Karduni, UNC Charlotte


Visual analytics (VA) systems combine computational support and human cognitive and perceptual skills to explore and analyze data. Many of these systems have been incorporating machine learning (ML) models and algorithms to introduce some level of automation to the analytical process. However, within this relationship, there are a number of aspects that can impact the effectiveness of the human-machine teaming, including (1) people’s domain and system expertise, (2) human biases, including cognitive and perceptual biases, and (3) trust in ML models and visual representation of data. Expertise, bias, and trust are intrinsically intertwined. Additionally, visual analytics systems are used in different fields and by people from various backgrounds, with different levels of domain expertise and experience with machine learning and visual analytics tools. This variety of experience and domain expertise among human users has opened the door for new research directions and challenges in the fields of visual analytics and machine learning. Designers who fail to consider the aforementioned diversities might introduce problems to the analysis effectiveness and user experience. Furthermore, experience and domain expertise might affect user trust in visual analytics tools; although, how and why they affect trust is still an open question. Trust will eventually affect how much the users would rely on and use the tool. While users will take advantage of their prior experiences to make better decisions with the assistance of analytic support, they might carry many cognitive biases that can negatively influence their decision-making or analysis process. Recent research shows trust in and reliance on the visual analytics systems/tools as well as user strategies and biases can be directly influenced by domain and system expertise (or lack of expertise). The goal of this workshop is to bring together researchers and practitioners from different disciplines to discuss and discover challenges in ML supported visual analytics tools and set the stage for future research directions and collaborations regarding these issues by proposing design guidelines, empirical findings, and VA techniques.

Human-Data Interaction

Lyn Bartram, Simon Fraser University
Sheelagh Carpendale, Simon Fraser University
Eun Kyoung Choe, University of Maryland
Bongshin Lee, Microsoft Research
Melanie Tory, Tableau Software


Interacting with data has become an integral part of many people’s daily lives. Not only working professionals but also lay individuals and data-enthusiasts engage in a broad range of activities that involve data, to achieve their goals in work, social, and personal contexts. The emerging area of human-data interaction encompasses all aspects where humans touch and engage with data, bringing new challenges and opportunities to visualization and visual analytics researchers and practitioners. Despite the huge potential in facilitating human-data interaction for more effective, productive, and rewarding experiences, current visualization research focuses on limited aspects of how people interact with data. This workshop aims to understand the broader ecosystem of data activities and explore the roles that visualization can play in a broader spectrum of human-data interaction.

Visualization for Social Good

Leilani Battle, University of Maryland
Michelle Borkin, Northeastern University
Michael Correll, Tableau Research
Lane Harrison, Worcester Polytechnic Institute
Evan Peck, Bucknell University


Visualization, like all fields connected with how we collect, interpret, and communicate data, has an immense potential impact on society, for good or for ill. We should make our values as a field explicit, criticize and critique the work we do that has a potential for harm, develop a pedagogy around the socially responsible design and use of visualization, and develop good patterns for fruitful collaboration with stakeholders working on key societal problems. We are the representatives of a long-time effort around better integrating social good and social causes into the culture and program of the IEEE VIS community, and raising awareness and recognition of how visualizations and visualization research can impact society. As part of this effort we organized the Visualization for Social Good tutorial at IEEE VIS 2019 and a panel on Visualization for Social Good at IEEE VIS 2020. As feedback from these efforts, we uncovered a perception that work using the power of visualization to further the public good was under-studied or otherwise not part of the main VIS conference. Participants felt that the main conference had a tendency to undervalue the social impact of the work they were doing, and explicitly asked us for a venue where they could present and discuss the social good work they were doing with their colleagues. We believe that a workshop venue could address this lack of representation in the main conference and give recognition to researchers whose work contributes towards social good, while at the same time highlighting a crucial and timely problem in visualization.

Fourth Workshop on Visualization for Communication (VisComm)

Barbara Millet, University of Miami
Jonathan Schwabish, Urban Institute
Adriana Arcia, Columbia University
Alvitta Ottley, Washington University in St. Louis


We propose a half-day workshop that will bring together practitioners and researchers from several fields to address the questions raised by the rapidly growing communicative uses of visualization (e.g., in infographics and health and news graphics). These questions span issues of audience, application, evaluation, understanding, and practice. To encourage participation from communities that do not typically attend IEEE VIS and write academic papers, we will accept short papers, briefs on works in progress, visual case studies, and recruit program committee members from those communities. We have organized this workshop before with great success: at VIS 2018 with around 70 participants and nine papers and posters, at VIS 2019 with around 60 participants (with the room at capacity and people turned away) and 13 papers and visual case studies, and at VIS 2020 with over 388 YouTube views (around 181 unique people watched during the live stream and over 55 unique viewers since then). Since the last workshop in October, its papers have been downloaded more than 1324 times.

MLUI 2021: Machine Learning from User Interaction for Visualization and Analytics

John Wenskovitch, Pacific Northwest National Laboratory, Virginia Polytechnic Institution and State University
Michelle Dowling, Grand Valley State University
Eli T Brown, DePaul University
Ab Mosca, Tufts University
Conny Walchshofer, Johannes Kepler University Linz
Marc Streit, Johannes Kepler University Linz
Kai Xu, Middlesex University


The high-level goal of this workshop is to bring together researchers from across the VIS community to share their knowledge and build collaborations at the intersection of the Machine Learning and Visualization fields, with a focus on learning from user interaction. Our hope in this workshop is to pull expertise from across all fields of VIS in order to generate open discussion about how we currently learn from user interaction and where we can go with future research. To achieve this goal, we propose a half-day workshop that incorporates paper presentations, keynotes, and free-form discussion. Our goal is to allow for the presentation of cutting-edge research, while also providing participants and speakers with time to exchange ideas and to discuss new research directions.

VisXVision: Workshop on Novel Directions in Vision Science and Visualization Research

Cindy Xiong, University of Massachusetts Amherst
Christine Nothelfer, Northwestern University
Madison Elliott, University of British Columbia
Danielle Albers Szafir, University of Colorado


Interdisciplinary work across vision science and visualization has provided a new lens to advance research methods and the empirical understanding of how people see and make sense of visualized data. By studying how people leverage visual cognition to perceive and interpret visualized data, researchers gain direct insight into how well visualizations achieve user goals. Topics in vision sciences, such as memory, ensemble coding, numerical cognition, saliency, color perception, search, and pattern recognition map directly to common challenges encountered in visualization research. Designers can use insights from vision research to inform effective visualization designs, which can in turn inspire new opportunities to understand how such visualizations work. Building on the growing interest in work at this intersection from both the vision science and visualization communities, this 2nd biennial workshop provides a venue to bring new researchers to IEEE VIS. We aim to discuss innovative discoveries at this intersection, share cutting-edge research methods/findings/proposals, and inspire new collaborations by leveraging the unique affordances of a hybrid conference, providing a platform for diverse voices.

2nd Workshop on Data Vis Activities to Facilitate Learning, Reflecting, Discussing, and Designing

When: Monday Oct 26, 8:00am-11:25am (Mountain Time - USA)

Samuel Huron, Telecom Paris Tech
Benjamin Bach, University of Edinburgh
Georgia Panagiotidou, KU Leuven
Mandy Keck, University of Applied Sciences Upper Austria
Jonathan C. Roberts, Bangor University
Sheelagh Carpendale, Simon Fraser University


This Data Vis Activities workshop is a sequel to our successful workshop at IEEE VIS 2020, focusing on methods and challenges for teaching and engaging with data visualization concepts, knowledge, and practices. Examples of such activities include sketching for design, constructing to learn, user interviews to elicit impressions and discussions to help developers understand requirements. Recent years have seen the emergence of such data visualization activities and associated research in different contexts, including education, visualization design, activism, self-reflection, and interdisciplinary collaboration. While our 2020 half-day workshop focused on providing activity research a dedicated platform and on start building a community, this workshop will focus on I) creating, running, and reflecting on activities especially in online settings, II) discussing higher-level issues in regards to activities, visualization education, and engagement, as well as III) discussing concrete future steps to build a permanent forum and community around these topics. Given that IEEE VIS 2021 is likely to run online, we design our workshop activities with an online setting in mind.


Jane L. Adams, University of Vermont
Lonni Besançon, Monash University, Linköpings Universitet
Michael Correll, Tableau Research
R. Jordan Crouser, Smith College
Charles Perin, University of Victoria
Paul Rosen, University of South Florida


Often the most transformative ideas and challenges come from unexpected and serendipitous sources. Yet, conferences are not often perceived of as a place for non-traditional, controversial, or outré work. Borrowing from the long-running and successful “alt.chi” model from the ACM SIGCHI conference, we propose an “alt.VIS” workshop as an avenue for surfacing work that would otherwise not find a home through the standard VIS conference review process.