Sponsor:

*VIRTUAL* – October 18th-22nd, 2021

Co-located with SVR 2021 e SBGames 2021

Certificates for SIBGRAPI 2021 are available through this link.

SIBGRAPI 2021 Workshop on Visual Analytics, Information Visualization and Scientific Visualization (WVIS)

Deadline for the submission of the short paper describing the research group: August 25, 2021
Deadline for the submission of the short paper describing the research group: September 4, 2021
Deadline for the submission of the short paper describing the research group: September 13, 2021

Notification of acceptance: September 9, 2021
Notification of acceptance: September 11, 2021
Notification of acceptance: September 20, 2021

Camera-ready due: September 20, 2021
Camera-ready due: September 29, 2021

SIBGRAPI 2021: October 18-22, 2021

All deadlines are 23:59 PDT.
Warning: incomplete or late submissions will not be considered.

This will be the 10th edition of the Workshop on Visual Analytics, Information Visualization and Scientific Visualization. Like in the previous editions, its purpose is to provide an opportunity for visualization researchers, practitioners, and the general public interested in data visualization to meet, discuss and share experiences, insights, and ideas in researching, developing, and applying visualization techniques to real-world problems.

With this edition of the workshop, we aim to bring together these researchers expecting that WVIS will serve as a forum for disseminating current work in the field while fostering new collaborations. Moreover, we expect the participation of students that might want an overview of research groups on data visualization, visual analytics, and all related areas.

Call for Labs/Groups Presentations

We invite contributions from researchers and professionals working on any topic related to data visualization, including but not limited to information visualization, scientific visualization, visual analytics, and immersive analytics.

Submissions must be in the form of a short paper in English or Portuguese and follow the SIBGRAPI formatting template but limited to 4 pages and not blind.

The paper content must include an introduction informing the focus of lab/group/company activities, main projects (short description), social, technological and/or scientific contributions, and bibliographic references. The submission must include the lab's webpage address in the last line of the authors' affiliation, and should be sent to the organizers by e-mail.

Presentation guidelines will be sent to authors of accepted contributions.

Preliminary Program

WVIS will be held on October 18th with the following tentative agenda:

14:15 - 14:30
Opening
14:30 - 15:30
15:30 - 17:30
Labs presentations session
17:30 - 18:25
Discussion on how to increase collaboration between groups
18:25 - 18:30
Closing

SIBGRAPI 2021 WVIS Invited Talk

Fernando Paulovich

October 18th (Mon)  |  14:30 to 15:30 BRT
Session Chair: Carla Dal Sasso Freitas (UFRGS)

Photo of Fernando Paulovich.

Fernando Paulovich
Dalhousie University, Canada

Talk Title: From Visual Analytics to Explainable AI: the Ingredients of More Reliable Classification Models

Talk Abstract: In recent decades, classification models have proven to be essential machine learning tools due to their potential and applicability in various domains. In these years, the general direction of most researchers has been to improve quantitative metrics, despite the lack of information about models' inner workings such metrics convey. This paradigm is shifting, and strategies beyond tables and numbers to help interpret model decisions are gaining importance. As part of this trend, visualization and visual analytics tools and techniques have been widely used and shown to be essential ingredients for implementing the so-called explainable Artificial Intelligence (XAI) concept. In this talk, I will introduce the idea of visual analytics and discuss how it has been used to interpret classification models, support the understanding of models' general behavior, and audit the produced results to increase confidence in the predictive analytics process.

Short Bio: Fernando V. Paulovich is an associate professor and Canada Research Chair in Data Visualization at the Faculty of Computer Science, Dalhousie University, and head of the Visualization and Visual Analytics (VVA) lab. Over the past ten years, he has been researching in the field of computational visualization, more specifically information visualization, visual analytics and visual data mining. His focus is on integrating machine learning and visualization tools and techniques, taking advantage of the “intelligence” provided by machine learning approaches, and of user knowledge by means of interactions with visual representations, helping people to understand and take full advantage of this "brave new information world."

Organizing committee

Carla Maria Dal Sasso Freitas (UFRGS) - carla@inf.ufrgs.br

Celmar Guimarães da Silva (UNICAMP) - celmar@ft.unicamp.br

Marcos Lage (UFF) - mlage@ic.uff.br

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