Aviz (Analysis and VIsualiZation) is a multidisciplinary project that seeks to improve visual exploration and analysis of large, complex datasets by tightly integrating analysis methods with interactive visualization.
Our work has the potential to affect practically all human activities for and during which data is collected and managed and subsequently needs to be understood. Often data-related activities are characterized by access to new data for which we have little or no prior knowledge of its inner structure and content. In these cases, we need to interactively explore the data first to gain insights and eventually be able to act upon the data contents. Interactive visual analysis is particularly useful in these cases where automatic analysis approaches fail and human capabilities need to be exploited and augmented.
Within this research scope Aviz focuses on five research themes:
Methods to visualize and smoothly navigate through large datasets;
Efficient analysis methods to reduce huge datasets to visualizable size;
Visualization interaction using novel capabilities and modalities;
Evaluation methods to assess the effectiveness of visualization and analysis methods and their usability;
Engineering tools for building visual analytics systems that can access, search, visualize and analyze large datasets with smooth, interactive response.
Aviz's research on Visual Analytics is organized around five main Research Themes:
Large data sets challenge current visualization and analysis methods. Understanding the structure of a graph with one million vertices is not just a matter of displaying the vertices on a screen and connecting them with lines. Current screens only have around two million pixels. Understanding a large graph requires both data reduction to visualize the whole and navigation techniques coupled with suitable representations to see the details. These representations, aggregation functions, navigation and interaction techniques must be chosen as a coordinated whole to be effective and fit the user's mental map.
Aviz designs new visualization representations and interactions to efficiently navigate and manipulate large data sets.
Designing analysis components with interaction in mind has strong implications for both the algorithms and the processes they use. Some data reduction algorithms are suited to the principle of sampling, then extrapolating, assessing the quality and incrementally enhancing the computation: for example, all the linear reductions such as PCA, Factorial Analysis, and SVM, as well as general MDS and Self Organizing Maps. Aviz investigates the possible analysis processes according to the analyzed data types.
The importance of interaction to Visualization and, in particular, to the interplay between interactivity and cognition is widely recognized. However, information visualization interactions have yet to take full advantage of these new possibilities in interaction technologies, as they largely still employ the traditional desktop, mouse, and keyboard setup of WIMP (Windows, Icons, Menus, and a Pointer) interfaces. At Aviz we investigate in particular interaction through tangible and touch-based interfaces to data.
For several reasons appropriate evaluation of visual analytics solutions is not trivial. First, visual analytics tools are often designed to be applicable to a variety of disciplines, for various different data sources, and data characteristics, and because of this variety it is hard to make general statements. Second, in visual analytics the specificity of humans, their work environment, and the data analysis tasks, form a multi-faceted evaluation context which is difficult to control and generalize. This means that recommendations for visual analytics solutions are never absolute, but depend on their context.
In our work we systematically connect evaluation approaches to visual analytics research—we strive to develop and use both novel as well as establish mixed-methods evaluation approaches to derive recommendations on the use of visual analytics tools and techniques. Aviz regularly published user studies of visual analytics and interaction techniques and takes part in dedicated workshops on evaluation.
for building visual analytics systems that can access, search, visualize and analyze large data sets with smooth, interactive response.
Currently, databases, data analysis and visualization all use the concept of data tables made of tuples and linked by relations. However, databases are storage-oriented and do not describe the data types precisely. Analytical systems describe the data types precisely, but their data storage and computation model are not suited to interactive visualization. Visualization systems use in-memory data tables tailored for fast display and filtering, but their interactions with external analysis programs and databases are often slow.
Aviz seeks to merge three fields: databases, data analysis and visualization. Part of this merging involves using common abstractions and interoperable components. This is a long-term challenge, but it is a necessity because generic, loosely-coupled combinations will not achieve interactive performance.
Aviz's approach is holistic: these five themes are facets of building an analysis process optimized for discovery. All the systems and techniques Aviz designs support the process of understanding data and forming insights while minimizing disruptions during navigation and interaction.
The scientific foundations of Visual Analytics lie primarily in the domains of Visualization and Data Mining. Indirectly, it inherits from other established domains such as graphic design, Exploratory Data Analysis (EDA), statistics, Artificial Intelligence (AI), Human-Computer Interaction (HCI), and Psychology.
The use of graphic representation to understand abstract data is a goal Visual Analytics shares with Tukey's Exploratory Data Analysis (EDA) , graphic designers such as Bertin and Tufte , and HCI researchers in the field of Information Visualization .
EDA is complementary to classical statistical analysis. Classical statistics starts from a problem, gathers data, designs a model and performs an analysis to reach a conclusion about whether the data follows the model. While EDA also starts with a problem and data, it is most useful before we have a model; rather, we perform visual analysis to discover what kind of model might apply to it. However, statistical validation is not always required with EDA; since often the results of visual analysis are sufficiently clear-cut that statistics are unnecessary.
Visual Analytics relies on a process similar to EDA, but expands its scope to include more sophisticated graphics and areas where considerable automated analysis is required before the visual analysis takes place. This richer data analysis has its roots in the domain of Data Mining, while the advanced graphics and interactive exploration techniques come from the scientific fields of Data Visualization and HCI, as well as the expertise of professions such as cartography and graphic designers who have long worked to create effective methods for graphically conveying information.
The books of the cartographer Bertin and the graphic designer Tufte are full of rules drawn from their experience about how the meaning of data can be best conveyed visually. Their purpose is to find effective visual representation that describe a data set but also (mainly for Bertin) to discover structure in the data by using the right mappings from abstract dimensions in the data to visual ones.
For the last 25 years, the field of Human-Computer Interaction (HCI) has also shown that interacting with visual representations of data in a tight perception-action loop improves the time and level of understanding of data sets. Information Visualization is the branch of HCI that has studied visual representations suitable to understanding and interaction methods suitable to navigating and drilling down on data. The scientific foundations of Information Visualization come from theories about perception, action and interaction.
Several theories of perception are related to information visualization such as the “Gestalt” principles, Gibson's theory of visual perception and Triesman's “preattentive processing” theory . We use them extensively but they only have a limited accuracy for predicting the effectiveness of novel visual representations in interactive settings.
Information Visualization emerged from HCI when researchers realized that interaction greatly enhanced the perception of visual representations.
To be effective, interaction should take place in an interactive loop faster than 100ms. For small data sets, it is not difficult to guarantee that analysis, visualization and interaction steps occur in this time, permitting smooth data analysis and navigation. For larger data sets, more computation should be performed to reduce the data size to a size that may be visualized effectively.
In 2002, we showed that the practical limit of InfoVis was on the order of 1 million items displayed on a screen . Although screen technologies have improved rapidly since then, eventually we will be limited by the physiology of our vision system: about 20 millions receptor cells (rods and cones) on the retina. Another problem will be the limits of human visual attention, as suggested by our 2006 study on change blindness in large and multiple displays . Therefore, visualization alone cannot let us understand very large data sets. Other techniques such as aggregation or sampling must be used to reduce the visual complexity of the data to the scale of human perception.
Abstracting data to reduce its size to what humans can understand is the goal of Data Mining research. It uses data analysis and machine learning techniques. The scientific foundations of these techniques revolve around the idea of finding a good model for the data. Unfortunately, the more sophisticated techniques for finding models are complex, and the algorithms can take a long time to run, making them unsuitable for an interactive environment. Furthermore, some models are too complex for humans to understand; so the results of data mining can be difficult or impossible to understand directly.
Unlike pure Data Mining systems, a Visual Analytics system provides analysis algorithms and processes compatible with human perception and understandable to human cognition. The analysis should provide understandable results quickly, even if they are not ideal. Instead of running to a predefined threshold, algorithms and programs should be designed to allow trading speed for quality and show the tradeoffs interactively. This is not a temporary requirement: it will be with us even when computers are much faster, because good quality algorithms are at least quadratic in time (e.g. hierarchical clustering methods). Visual Analytics systems need different algorithms for different phases of the work that can trade speed for quality in an understandable way.
Designing novel interaction and visualization techniques to explore huge data sets is an important goal and requires solving hard problems, but how can we assess whether or not our techniques and systems provide real improvements? Without this answer, we cannot know if we are heading in the right direction. This is why we have been actively involved in the design of evaluation methods for information visualization , , , , . For more complex systems, other methods are required. For these we want to focus on longitudinal evaluation methods while still trying to improve controlled experiments.
We design novel visualization and interaction techniques (see, for example, Figure ). Many of these techniques are also evaluated throughout the course of their respective research projects. We cover application domains such as sports analysis, digital humanities, fluid simulations, and biology. A focus of Aviz' work is the improvement of graph visualization and interaction with graphs. We further develop individual techniques for the design of tabular visualizations and different types of data charts. Another focus is the use of animation as a transition aid between different views of the data. We are also interested in applying techniques from illustrative visualization to visual representations and applications in information visualization as well as scientific visualization.
Evaluation methods are required to assess the effectiveness and usability of visualization and analysis methods. Aviz typically uses traditional HCI evaluation methods, either quantitative (measuring speed and errors) or qualitative (understanding users tasks and activities). Moreover, Aviz is also contributing to the improvement of evaluation methods by reporting on the best practices in the field, by co-organizing workshops (BELIV 2010, 2012, 2014, 2016) to exchange on novel evaluation methods, by improving our ways of reporting, interpreting and communicating statistical results, and by applying novel methodologies, for example to assess visualization literacy.
We want to understand the requirements that software and hardware architectures should provide to support exploratory analysis of large amounts of data. So far, “big data” has been focusing on issues related to storage management and predictive analysis: applying a well-known set of operations on large amounts of data. Visual Analytics is about exploration of data, with sometimes little knowledge of its structure or properties. Therefore, interactive exploration and analysis is needed to build knowledge and apply appropriate analyses; this knowledge and appropriateness is supported by visualizations. However, applying analytical operations on large data implies long-lasting computations, incompatible with interactions, and generates large amounts of results, impossible to visualize directly without aggregation or sampling. Visual Analytics has started to tackle these problems for specific applications but not in a general manner, leading to fragmentation of results and difficulties to reuse techniques from one application to the other. We are interested in abstracting-out the issues and finding general architectural models, patterns, and frameworks to address the Visual Analytics challenge in more generic ways.
We want to empower humans to make use
of data using different types of display media and to enhance how
they can understand and visually and interactively explore
information. This includes novel display equipment and accompanying input techniques. The Aviz team specifically focuses on the exploration of the use of large displays in visualization contexts as well as emerging physical and tangible visualizations. In terms of interaction modalities our work focuses on using touch and tangible interaction. Aviz participates to the Digiscope project that funds 11 wall-size displays at multiple places in the Paris area (see http://
More cross-fertilization is needed between psychology and information visualization. The only key difference lies in their ultimate objective: understanding the human mind vs. helping to develop better tools. We focus on understanding and using findings from psychology to inform new tools for information visualization. In many cases, our work also extends previous work in psychology. Our approach to the psychology of information visualization is largely holistic and helps bridge gaps between perception, action and cognition in the context of information visualization. Our focus includes the perception of charts in general, perception in large display environments, collaboration, perception of animations, how action can support perception and cognition, and judgment under uncertainty.
Steve Haroz joined Aviz as a research scientist (SRP) for three years.
Catherine Plaisant joined Aviz as an International Chair for 5 years.
The team welcomed two invited professors (Claudio Silva and Michael McGuffin).
Aviz members presented seven papers at IEEE VIS 2018 and won a best paper award at Eurovis 2018.
Former Aviz PhD student Lonni Besançon received a PhD thesis prize honorable mention award from GDR, AFIG, AFRV, and EGFR for his thesis “An interaction Continuum for 3D Data Visualization.”
Aviz started an Associated Team with the ilab at the University of Calgary on the topic of Situated and Embedded Visualization.
Keyword: Information visualization
Functional Description: The goal of Cartolabe is to build a visual map representing the scientific activity of an institution/university/domain from published articles and reports. Using the HAL Database and building upon the AnHALytics processing chain, Cartolabe provides the user with a map of the thematics, authors and articles and their dynamics along time. ML techniques are used for dimensionality reduction, cluster and topics identification, visualisation techniques are used for a scalable 2D representation of the results.
News Of The Year: Improvement of the graphical interface
Contact: Philippe Caillou
BitConduite Bitcoin explorer
Keywords: Data visualization - Clustering - Financial analysis - Cryptocurrency
Functional Description: BitConduite is a web-based visual tool that allows for a high level explorative analysis of the Bitcoin blockchain. It offers a data transformation back end that gives us an entity-based access to the blockchain data and a visualization front end that supports a novel high-level view on transactions over time. In particular, it facilitates the exploration of activity through filtering and clustering interactions. This gives analysts a new perspective on the data stored on the blockchain.
Contact: Petra Isenberg
Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We use a novel model called multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. This library implements our declarative model: a simple yet expressive JSON grammar associated with visual semantics, that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.
More on the project page: Multiclass Density Maps.
We presented the first empirical study on using color manipulation and stylization to make surgery images more palatable . While aversion to such images is natural, it limits many people's ability to satisfy their curiosity, educate themselves, and make informed decisions. We selected a diverse set of image processing techniques, and tested them both on surgeons and lay people. While many artistic methods were found unusable by surgeons, edge-preserving image smoothing gave good results both in terms of preserving information (as judged by surgeons) and reducing repulsiveness (as judged by lay people). Color manipulation turned out to be not as effective.
This study is an initial investigation but opens up exciting avenues for future research. These include supporting surgery videos, other types of medical images than open surgery (e.g., skin diseases), as well as disturbing imagery outside the medical domain, such as offensive user-generated content that can psychologically impact professionals who monitor it.
All supplemental material is on the OSF page: osf.io/4pfes/.
We explored how to rigorously evaluate multidimensional visualizations for their ability to support decision making . We first defined multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identified which of the existing multidimensional visualizations are compatible with such tasks, and evaluated three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consisted in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants were then given multi-attribute choice tasks consisting of choosing holiday packages. We assessed decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allowed participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones.
All supplemental material is on the project web page: aviz.fr/dm.
We provided a reappraisal of Tal and Wansink’s study “Blinded with Science”, where seemingly trivial charts were shown to increase belief in drug efficacy, presumably because charts are associated with science. Through a series of four replications conducted on two crowdsourcing platforms, we investigated an alternative explanation, namely, that the charts allowed participants to better assess the drug’s efficacy . Considered together, our experiments suggested that the chart seems to have indeed promoted understanding, although the effect is likely very small. Meanwhile, we were unable to replicate the original study’s findings, as text with chart appeared to be no more persuasive – and sometimes less persuasive – than text alone. This suggests that the effect may not be as robust as claimed and may need specific conditions to be reproduced. Regardless, within our experimental settings and considering our study as a whole (N = 623), the chart’s contribution to understanding was clearly larger than its contribution to persuasion.
The main lesson from our study is that with charts, the peripheral route of persuasion cannot be studied independently from the central route: in order to establish that a chart biases judgment, it is necessary to also rigorously establish that it does not aid comprehension. Our replication also opens many relevant questions for infovis. Are charts really associated with science? More generally, what associations do charts or visualizations trigger depending on their visual design? When exactly is a chart trivial?
All supplemental material is on the project web page: aviz.fr/blinded.
We discussed the concept of directness in the context of spatial interaction with visualization. In particular, we proposeed a model (see Figure ) that allows practitioners to analyze and describe the spatial directness of interaction techniques, ultimately to be able to better understand interaction issues that may affect usability. To reach these goals, we distinguished between different types of directness. Each type of directness depends on a particular mapping between different spaces, for which we consider the data space, the visualization space, the output space, the user space, the manipulation space, and the interaction space. In addition to the introduction of the model itself, we also showed how to apply it to several real-world interaction scenarios in visualization, and thus discussed the resulting types of spatial directness, without recommending either more direct or more indirect interaction techniques. In particular, we demonstrated descriptive and evaluative usage of the proposed model, and also briefly discussed its generative usage.
More on the project Web page: https://tobias.isenberg.cc/VideosAndDemos/Bruckner2018MSD.
We presented an approach to represent DNA nanostructures in varying forms of semantic abstraction, describe ways to smoothly transition between them, and thus create a continuous multiscale visualization and interaction space for applications in DNA nanotechnology. This new way of observing, interacting with, and creating DNA nanostructures enables domain experts to approach their work in any of the semantic abstraction levels, supporting both low-level manipulations and high-level visualization and modifications. Our approach allows them to deal with the increasingly complex DNA objects that they are designing, to improve their features, and to add novel functions in a way that no existing single-scale approach offers today. For this purpose we collaborated with DNA nanotechnology experts to design a set of ten semantic scales (see Figure ). These scales take the DNA's chemical and structural behavior into account and depict it from atoms to the targeted architecture with increasing levels of abstraction. To create coherence between the discrete scales, we seamlessly transition between them in a well-defined manner. We used special encodings to allow experts to estimate the nanoscale object's stability. We also added scale-adaptive interactions that facilitate the intuitive modification of complex structures at multiple scales. We demonstrate the applicability of our approach on an experimental use case. Moreover, feedback from our collaborating domain experts confirmed an increased time efficiency and certainty for analysis and modification tasks on complex DNA structures. Our method thus offers exciting new opportunities with promising applications in medicine and biotechnology.
More on the project Web page: https://tobias.isenberg.cc/VideosAndDemos/Miao2018MVS.
We presented a novel visualization concept for DNA origami structures that integrates a multitude of representations into a DimSUM. This novel abstraction map (see Figure ) provides means to analyze, smoothly transition between, and interact with many visual representations of the DNA origami structures in an effective way that was not possible before. DNA origami structures are nanoscale objects, which are challenging to model in silico. In our holistic approach we seamlessly combined three-dimensional realistic shape models, two-dimensional diagrammatic representations, and ordered alignments in one-dimensional arrangements, with semantic transitions across many scales. To navigate through this large, two-dimensional abstraction map we highlighted locations that users frequently visit for certain tasks and datasets. Particularly interesting viewpoints can be explicitly saved to optimize the workflow. We have developed DimSUM together with domain scientists specialized in DNA nanotechnology. In the paper we discussed our design decisions for both the visualization and the interaction techniques. We demonstrateed two practical use cases in which our approach increases the specialists' understanding and improves their effectiveness in the analysis. Finally, we discussed the implications of our concept for the use of controlled abstraction in visualization in general.
More on the project Web page: https://tobias.isenberg.cc/VideosAndDemos/Miao2018DDS.
We discussed the concept of directness in the context of spatial interaction with visualization (Figure ). In particular, we proposed a model (autoreffig:directness) that allows practitioners to analyze and describe the spatial directness of interaction techniques, ultimately to be able to better understand interaction issues that may affect usability. To reach these goals, we distinguished between different types of directness. Each type of directness depends on a particular mapping between different spaces, for which we consider the data space, the visualization space, the output space, the user space, the manipulation space, and the interaction space. In addition to the introduction of the model itself, we also showed how to apply it to several real-world interaction scenarios in visualization, and thus discussed the resulting types of spatial directness, without recommending either more direct or more indirect interaction techniques. In particular, we demonstrated descriptive and evaluative usage of the proposed model, and also briefly discussed its generative usage.
More on the project Web page: https://tobias.isenberg.cc/VideosAndDemos/Bruckner2018MSD.
Those of us who study large effects may believe ourselves to be unaffected by the reproducibility problems that plague other areas. However, we will argue that initiatives to address the reproducibility crisis, such as preregistration and data sharing, are worth adopting even under optimistic scenarios of high rates of replication success. We searched the text of articles published in the Journal of Vision from January through October of 2018 for URLs (our code is here: https://
In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we experimentally compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.
More on the project Web page.
Tobias Isenberg received an equipment grant from STIC, Paris-Saclay, for approx. EUR 5K
Naviscope Inria Project Lab on Image-guided NAvigation and VIsualization of large data sets in live cell imaging and microSCOPy; collaboration with several Inria project teams and external collaborators; this grant supports a PhD position and funds travel and equipment.
Title: Interactive and Visual Analysis of Networks
Programm: CHIST-ERA
Duration: May 2018 - April 2021
Coordinator: Dr. Torsten Möller, Uni Wien, Austria
Partners:
EPFL, Switzerland
Inria France
Uni Wien, Austria
Inria contact: Jean-Daniel Fekete
The main goal of IVAN is to create a visual analysis system for the exploration of dynamic or time-dependent networks (from small to large scale). Our contributions will be in three principle areas:
novel algorithms for network clustering that are based on graph harmonic analysis and level-of-detail methods;
the development of novel similarity measures for networks and network clusters for the purpose of comparing multiple network clusterings and the grouping (clustering) of different network clusterings; and
a system for user-driven analysis of network clusterings supported by novel visual encodings and interaction techniques suitable for exploring dynamic networks and their clusterings in the presence of uncertainties due to noise and uncontrolled variations of network properties.
Our aim is to make these novel algorithms accessible to a broad range of users and researchers to enable reliable and informed decisions based on the network analysis.
Illustrare project co-funded by ANR, France, and FWF, Austria, funding a PhD position and funds for travel and equipment. The project investigates integrative visual abstraction of molecular data and is a collaboration with TU Wien, Austria
Title: Situated and Embedded Visualization for Data Analysis
International Partner (Institution - Laboratory - Researcher):
University of Calgary (Canada) - ILab - Wesley Willett
Start year: 2018
See also: http://
The goal of this joint work between the Aviz team at Inria Saclay and the ILab at the University of Calgary is to develop and study situated data visualizations to address the limitations of traditional platforms of data analytics. In a situated data visualization, the data is directly visualized next to the physical space, object, or person it refers to. Situated data visualizations can surface information in the physical environment and allow viewers to interpret data in-context, monitor changes over time, make decisions, and act on the physical world in response to the insights gained. However, research on this topic remains scarce and limited in scope. We will build on our track record of successfull collaborations to jointly develop situated visualization as a novel research direction. The objective for the first year is to design and implement situated visualizations to support health and aging. Our joint work is expected generate benefits at multiple levels, including to society and industry (by empowering individuals and professionals with technology), to the scientific community (by developing a new research direction), to the academic partners (by reinforcing existing research links and establishing them as leaders on the topic), and to students (by providing them with unique training opportunities with a diverse team of world-class researchers).
University of Maryland (USA), University of Roma (Italy), TU Darmstadt (Germany): Jean-Daniel Fekete Fekete collaborates with Leilani Battle, Giuseppe Santucci, Carsten Binnig and colleagues on the design of database benchmarks to better support visualization;
University of Seoul (Korea): Jean-Daniel Fekete collaborates with Jaemin Jo and Jinwook Seoh on progressive algorithms and visualization techniques;
University of Bari (Italy): Jean-Daniel Fekete collaborates with Paolo Buono on hypergraph visualization;
Stanford University. Pierre Dragicevic and Jean-Daniel Fekete collaborate with Sean Follmer on swarm user interfaces.
Hasso Plattner Institute. Pierre Dragicevic and Tobias Isenberg collaborate with Amir Semmo on stylization filters for facilitating the examination of disturbing visual content.
University of Minnesota, USA: Tobias Isenberg is collaborating with Daniel F. Keefe on topics of the interactive exploration of 3D data.
University of Granada, Spain: Tobias Isenberg is collaborating with Domingo Martin and German Arroyo on digital stippling.
The University of Sydney, Australia. Steve Haroz collaborate with Alex Holcombe on analyzing open practices in vision science.
Massachusetts Institute of Technology (CSAIL). Steve Haroz collaborates with Aude Oliva on investigating the impact of titles on memory of visualized data.
University of Washington, University of Zurich and University of Toronto. Pierre Dragicevic and Steve Haroz collaborate with Matthew Kay and Chat Wacharamanotham on transparent statistical reporting and efficient statistical communication. Pierre Dragicevic collaborates with Matthew Kay and Fanny Chevalier on supporting research transparency with interactive research papers.
University of Calgary. Pierre Dragicevic, Tobias Isenberg, and Petra Isenberg collaborate with Wesley Willett, Sheelagh Carpendale, and Lora Oehlberg on situated data visualization.
Microsoft Research Redmond and University of Maryland. Petra Isenberg collaborate with Bongshin Lee, Mathieu Brehmer, and Eun Kyoung Choe on Mobile Visualization
Microsoft Research Redmond. Petra Isenberg and Tanja Blascheck collaborate with Bongshin Lee on Micro Visualizations for Smartwatches
Claudio Silva (August 2018 – June 2019): Sabbatical from New York University (USA). Also, invited professor through a DigiCosme grant for 3 months. Claudio Silva is spending one year with Aviz. We launched a bi-weekly seminar on explainable machine-learning with visualization.
Michael McGuffin (October – November): visit from ETS Montreal (Canada). Michael McGuffin has spent a month with Aviz working on augmented reality and visualization, collaborating with Pierre Dragicevic, Jean-Daniel Fekete, and students.
Jung Nam from the University of Minnesota visited for 3 months in the summer of 2018. His work centered on the use of storytelling mechanisms to support and communicate results of the exploration 3D data. This collaboration is still ongoing.
Jean-Daniel Fekete co-organized the Dagstuhl Seminar on Progressive Data Analysis and Visualization in 2018
Jean-Daniel Fekete co-organized the Dagstuhl Seminar on Provenance and Logging for Sense Making in 2018
Pierre Dragicevic co-organized the Journée Visu 2018.
Tobias Isenberg co-organized for BELIV 2018.
Petra Isenberg was paper chair for InfoVis 2019.
Jean-Daniel Fekete was a member of the program committee for VIS 2018
Jean-Daniel Fekete was a member of the best paper committee for PacificVis 2018.
Pierre Dragicevic was a member of the program committee for VIS 2018.
Tobias Isenberg was a member of the program committee for ACM/Eurographics Expressive 2018.
Tobias Isenberg was a member of the program committee for IEEE VR 2018.
Tobias Isenberg was a member of the program committee for EuroVis 2018.
Tobias Isenberg was a member of the program committee for IVAPP 2018.
Petra Isenberg was a member of the program committee for CHI.
Jean-Daniel Fekete reviewed for CHI and PacificVis
Pierre Dragicevic reviewed for VIS, UIST, EuroVis, alt.CHI, EICS, TEI, IHM.
Tobias Isenberg reviewed for CHI, Expressive, InfoVis, PacificVis, SciVis, VAST, VISAP, and VR.
Xiyao Wang reviewed for IHM, and TEI.
Petra Isenberg reviewed for: CHI, EuroVA, EuroVis, ISS
Pierre Dragicevic is member of the editorial board of the Journal of Perceptual Imaging (JPI).
Pierre Dragicevic is member of the editorial board of the Springer Human–Computer Interaction Series (HCIS).
Tobias Isenberg is member of the editorial board of Elsevier's Computers & Graphics journal.
Petra Isenberg is member of the editorial board of IEEE Transactions on Visualization and Computer Graphics.
Petra Isenberg is Associate Editor in Chief for IEEE Computer Graphics and Applications.
Jean-Daniel Fekete reviewed for TVCG
Pierre Dragicevic reviewed for TOCHI, TVCG, JPI.
Steve Haroz reviewed for Cognition, Journal of Experimental Psychology: General, Meta-Psychology
Petra Isenberg reviewed for: Morgan Claypool (book proposal)
Jean-Daniel Fekete: Seminar EDF Lab Chatou "Progressive data analysis: a new language paradigm for scalability in exploratory data analysis", Chatou, Sep. 19, 2018
Jean-Daniel Fekete: seminaire de recherche Medialab, « Nouvelles visualisations de réseaux pour de nouvelles formes d’explorations et d’analyses », Sciences Po Paris, April 3rd, 2018
Jean-Daniel Fekete: Delft Data Science Seminar: Visual Data Science and its role in Computational Medicine The role of visualization in the hypothetico-deductive method, Delft University of Technology, January 6th, 2018.
Tobias Isenberg: “Illustrative Visualization and Abstraction of Scientific Data”. M3DISIM joined Inria/Ecole Polytechnique research team, Orsay, France, November 2018.
Tobias Isenberg: “Illustrative Visualization and Abstraction of Scientific Data”. GraphDeco research team, Sophia-Antipolis, France, December 2018.
Steve Haroz: “Open Practices in Visualization Research”. Panel discussion at Evaluation and Beyond - Methodological Approaches for Visualization workshop, 21 Oct 2018.
Petra Isenberg: “Evaluation in Visualization”. Keynote at the EMBL-EBI Workshop on Innovations in data visualisation for drug discovery, June 2018.
Petra Isenberg: “Micro Visualizations for Pervasive and Mobile Data Exploration”. Seminar Series Talk, Technical University of Dresden, May 2018.
Jean-Daniel Fekete is the Chair of the Steering Committee of the IEEE Information Visualization Conference.
Jean-Daniel Fekete is member of the VIS Executive Committee.
Jean-Daniel Fekete is a member of the Eurographics Publication Board.
Jean-Daniel Fekete is the Chair of the EuroVis Best Phd Award.
Tobias Isenberg has been a member of the Executive Committee of the Visualization and Computer Graphics Technical Committee of the IEEE Computer Society and served as Publications Chair.
Tobias Isenberg is a member of the Steering Committee of Expressive (Joined Symposium on Computational Aesthetics, Sketch-Based Interfaces & Modeling, and Non-Photorealistic Animation & Rendering).
Steve Haroz is the Open Practices chair of the IEEE Information Visualization conference.
Pierre Dragicevic reviewed for an NSERC Discovery Grant proposal.
Pierre Dragicevic: Member of the Commission Consultative de Spécialistes de l'Université Paris-Sud (CCSU).
“Visualization and Visual Analytics” taught by Jean-Daniel Fekete, Master 2 in Data Science, 32 hours, École Polytechnique, France.
“Photorealistic Rendering” taught by Tobias Isenberg at Polytech Paris-Sud and Université Paris-Saclay, France.
“Introduction to Computer Graphics” taught by Tobias Isenberg at Polytech Paris-Sud, France.
“Non-Photorealistic Rendering” taught by Tobias Isenberg at the University of Granada, Spain.
guest lecture on “Introduction to Data Visualization” taught by Tobias Isenberg at Université Côte d'Azur, Nice, France.
guest lecture on “Illustrative Visualization” taught by Tobias Isenberg at ENSTA, France.
“Interactive Information Visualization” taught by Petra Isenberg at Université Paris Sud.
“Visual Analytics” taught by Petra Isenberg at CentraleSupelec.
PhD: Marc Barnabé, Multiscale reconstruction of microbial ecosystems using semi-supervised machine learning, Université Paris-Sud, 2018, Jean-Daniel Fekete, Evelyne Lutton, INRA.
PhD in progress: Mickaël Sereno, Collaborative Data Exploration and Discussion Supported by AR, Univ. Paris-Sud; 2018, Tobias Isenberg
PhD in progress: Xiyao Wang, Augmented Reality Environments for the Interactive Exploration of 3D Data, Univ. Paris-Sud; 2017, Tobias Isenberg
PhD in progress: Sarkis Halladjian, Spatially Integrated Abstraction of Genetic Molecules, Univ. Paris-Sud; 2017, Tobias Isenberg
PhD in progress: Haichao Miao, Visual Abstraction and Modeling for DNA Nanotechnology, TU Wien, Austria, 2016, Tobias Isenberg
Jean-Daniel Fekete: Member of the PhD committee of Dr. Marion Dumont.
Jean-Daniel Fekete: Member of the PhD committee of Dr. Germán Leiva.
Jean-Daniel Fekete: Member of the HdR committee of Dr. Eric Lecolinet.
Pierre Dragicevic: Member of the hiring committee for Concours CRCN 2018 Centre Inria Rennes Bretagne Atlantique.
Pierre Dragicevic: PhD defense committee of Bruno Fruchard (Télécom Paris Tech).
Pierre Dragicevic: Mid-term PhD evaluation committee of Stacy Hsueh (Université Paris-Saclay).
Pierre Dragicevic: Reviewer for Marguerite Peron's M2 internship.
Petra Isenberg: Member of the PhD committee of Dr. Ulrike Kister
Petra Isenberg: Reviewer Master Pro thesis, Ayoub Jaa, Université Paris Sud
Petra Isenberg: Co-Supervisor Master thesis, Mathieu Louvet, Unversité Paris Sud
Petra Isenberg: Co-Supervisor Master thesis, Mina Alipour, CentraleSupelec
Petra Isenberg: Academic Tutor Master thesis: Marvin Rea
Lonni Besançon’s PhD work was featured in Le Monde's Binaire blog.
Steve Haroz was interviewed on the Data Stories podcast, October 10, 2018
Pierre Dragicevic and Yvonne Jansen: the data physicalization wiki and the List of Physical Visualizations and Related Artefacts (500 weekly visits) are continuously being updated.