Infographics are widely used to summarize complex data, illustrate problems and solutions, and tell stories over data. Our goal in this project is to investigate interactive tools and techniques that can help data journalists, infographic artists, and, by extension, data scientists and everyday people produce creative visualizations for communication purposes, e.g., to inform the public about the evolution of a pandemic and help novices interpret global-warming predictions or reflect on personal data [Lupi and Pocavec, 2018].
A key challenge for many infographic creators is how to conceive and implement original, fresh visual representations that highlight the unique properties of a dataset. Professionals commonly switch between sketches on paper and computers to reach a new visualization design [Landers and Heller, 2014]. Computer programs are powerful tools that help them generate solutions keeping a direct binding with the underlying data. But many design experts first start by exploring visualization solutions through hand-drawn sketches. Before having access to the actual data, sketches can help them “visualize the architecture of the infographics and cultivate ideas for shaping the data visually,” while later, sketching with data can “help raise new questions about the data itself” [Lupi, 2015]. Such professional workflows provide inspiration for future visualization authoring tools with users who are not necessarily design experts. Unfortunately, current visualization systems target data-exploration and data-analysis tasks and fail to adapt to communication [Kosara, 2016] and design purposes [Bigelow, 2014]. The process of creating an original infographic can be extremely manual, involving multiple tools that are largely disconnected from the underlying data [Chevalier et al., 2018].
The goal of this Ph.D. thesis is to bring infographics design and visualization tools closer together [Cairo, 2012]. We want to address the more ambitious goal of computer-aided design that treats infographic creation as a visual-thinking process [Ware, 2008]. This process starts from sketches and progressively moves to data and generative parametric instructions, which can then re-feed the designer’s sketches. We are also interested in how such tools can benefit users with no design expertise in different contexts, such as educators who want to instill data science and visualization knowledge through their own informal infographics, or common people who want to track and visually express their personal data [Lupi and Pocavec, 2018].
Expected results include: (i) a better understanding of how infographic artists iterate on their design sketches, (ii) techniques for semi-automatically extracting the relevant graphical properties of visualization sketches and structuring their constraints, (iii) techniques for reshaping sketch-driven visualizations, and (iv) and interactive tools that allow users to design creative infographics.
The candidate is expected to have a Master degree (M2-level for the French system) and background in Human-Computer Interaction, Information Visualization, and/or Computer Graphics. The candidate must have solid programming skills and be fluent in English (reading, writing, and oral). No French knowledge is required.
A. Bigelow, S. Drucker, D. Fisher, and M. Meyer. Reflections on how designers design with data. ACM AVI, pp. 17–24, 2014.
A. Cairo. The Functional Art: An Introduction to Information Graphics and Visualization, New Riders, Aug 2012.
F. Chevalier, M. Tory, B. Lee, M. Dörk, J. Van Wijk, and J. Hullman. From Analysis to Communication: Supporting the Lifecycle of a Story. N. Henry Riche, C. Hurter, N. Diakopoulos, S. Carpendale (Eds.) Data-Driven Storytelling. A K Peters / CRC Press, 2018.
R. Kosara. Presentation-oriented visualization techniques. IEEE Computer Graphics and Applications, 36(1):80–85, Jan 2016.
R. Landers and S. Heller. Infographics Designers’ Sketchbooks. Adams Media, October. 2014.
G. Lupi. Sketching with data opens the mind’s eye. National Geographic, July 2015.
G. Lupi and S. Posavec. Observe, Collect, Draw!: A Visual Journal Diary, Princeton Architectural Press. Sep. 2018.
T. Tsandilas. StructGraphics: Flexible Visualization Design through Data-Agnostic and Reusable Graphical Structures. IEEE Transactions on Visualization and Computer Graphics, pp. 315-325, 2020.
T. Tsandilas, A. Bezerianos, and T. Jacob. SketchSliders: Sketching Widgets for Visual Exploration on Wall Displays. ACM Conference on Human Factors in Computing Systems (CHI), pp. 3255-3264, 2015.
H. Xia, N. Henry Riche, F. Chevalier, B. De Araujo, and D. Wigdor. DataInk: Direct and creative data-oriented drawing. ACM Conference on Human Factors in Computing Systems (CHI), pp. 223:1–223:13, 2018.
J. E. Zhang, N. Sultanum, A. Bezerianos, and F. Chevalier. DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations. ACM Conference on Human Factors in Computing Systems (CHI), pp. 1-13, 2020.
The Ph.D. thesis will be co-supervised by Theophanis Tsandilas (Inria) and Fanny Chevalier (University of Toronto). It can start any time between Sep 1 and Dec 1, 2021. To apply, please use the following link: jobs.inria.fr/public/classic/en/offres/2021-03647
We encourage you to add your CV, a motivation letter, and any information that could make your application stand out: links to projects or interactive prototypes, research reports (e.g., Master thesis or paper) that demonstrate your research experience, etc. We will accept applications until the position is filled. Do not hesitate to contact us for additional information.