2024Activity reportProject-TeamAVIZ

Language:

ProgressiVis relies on a Python interpreter that is convenient for quick prototyping. We are using many internal mechanisms of the Python language to create a toolkit that allows programming progressive applications in a convenient way.

Algorithms:

Existing libraries implementing algorithms are either “eager”, sometimes “online”, and more rarely “streaming”, but very few are compatible with our progressive paradigm. We already designed several progressive algorithms and want to further expand the range of algorithms we can support, and better understand how algorithms could be transformed into their progressive counterpart.

Progressive computation can trade speed, memory, and quality. When analysts want to compute rough estimates of their results quickly, several progressive algorithms offer parameters to control the tradeoffs. For example, our progressive k-nearest-neighbors algorithm 62 allows choosing the number of k-d-trees that influences the memory footprint, accuracy, and speed. Progressive data analysis also uses and adapts streaming algorithms, also called “data sketches” 57, that offer various tradeoffs between speed, memory, and quality.

Visualizations:

Like algorithms need to be adapted or transformed, visualization techniques and user interfaces also need to be adapted to be used in a progressive setting.

For the visualization pipeline, including the rendering phase, there has been previous work related to progressive layout computation and progressive rendering. For example, in network visualization, there is an old tradition of using iterative force-directed solvers and showing their results progressively when computing complex graph layouts. We generalize this approach to all the visualizations. Progressive rendering is popular in the real-time computer graphics and gaming domains, but new in visualization. We are designing visualization techniques based on GPU and accelerated algorithms to better render aggregated visualizations, such as heatmaps and sampled visualizations.

We have also started to explore the problem of requirements for progressive user interfaces 51, leading the community of researchers to understand that progressive systems need to provide effective assessments of the quality of their results to allow analysts to make early decisions. This justifies the next research topic on the management of uncertainties. We have also started to propose new data models for managing aggregated visualizations 63. We now continue our research to adapt existing visualization techniques and interactive environments to deal with progressive results and parameter steering.

(1) simple bar char. (2) with confidence intervals. (3) with recent history, and (4) with recent history and confidence intervals.

Management of uncertainties:

When performing exploratory data analysis on large data, analysts start by trying some algorithms with different parameters until they obtain useful results. Therefore, it is of paramount importance to be able to realize that a specific algorithm or a specific set of parameters is not leading to a useful result and to abort an analysis before it ends to explore other methods. On the other side, in many circumstances when data is “well-behaved”, progressive algorithms achieve very good results quickly, and analysts can make a decision before the end of the algorithms' execution. The assessment of a quality measure related to a computed result is a complex problem, already tackled by other communities (e.g., streaming, approximate queries).

Progressive Data Analysis adds two problems: (1) controlling uncertainty during progressive computations, and (2) conveying the results of this uncertainty in a way understandable by analysts, while avoiding possible cognitive biases such as priming and anchoring 65.

To control the uncertainty during the progression of a computation, methods related to Sequential Analysis 59 should be used. They are well-known in the area of clinical trials and have evolved recently to manage large amounts of data while controlling time or quality in Approximate Query Databases such as BlinkDB 50.

We have already shown that humans are quite good at deciding when to stop a progressive process to make an early decision using progressive bar charts  72 (fig:ExamplesOfProgressiveBarChartsWeStudied); humans are cognitively equipped to make sense of progressive processes. Additionally, visualization can help humans make sense of and interact with progressive processes, such as our new-history bar chart designs that help to render visually when a progressive process starts to converge, opening a new line of research to discover the possible forms of support.

6.1.1 Paper Awards

Aviz received three honorable mentions:

• Anne-Flore Cabouat, Tingying He, Petra Isenberg and Tobias Isenberg. “PREVis: Perceived readability evaluation for visualizations.” 8
• Gabriela Molina Leon, Anastasia Bezerianos , Olivier Gladin and Petra Isenberg. “Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics.’’ 14
• Zack While , Tanja Blascheck , Yujie Gong , Petra Isenberg and Ali Sarvghad . “Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups.’’ 36

6.1.2 Test Of Time Awards

Aviz received two Test Of Time Awards:

• Mohammad Ghoniem , Jean-Daniel Fekete and Philippe Castagliola . “A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations” 58 received the 20 years Test Of Time Award from IEEE VIS 2024
• Lingyun Yu , Konstantinos Efstathiou , Petra Isenberg and Tobias Isenberg. “Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input” 88 received the 12 years Test Of Time Award from IEEE VIS 2024.

6.1.3 Other Awards

Tobias Isenberg received a reviewing honorable mention award at EuroVis 2024.

7.1.1 ProgressiVis

• Name:
  progressive visualization and data analysis
• Keywords:
  Progressive visualization, Visualization, Visual analytics
• Functional Description:

  Instead of running algorithms to completion one after the other, as done in all existing scientific analysis systems, ProgressiVis modules run in short batches, each batch being only allowed to run for a specific quantum of time - typically 1 second - producing a usable result in the end, and yielding control to the next module. To perform the whole computation, ProgressiVis loops over the modules as many times as necessary to converge to a result that the analyst considers satisfactory.

  ProgressiVis relies on well known Python libraries, such as numpy,scipy, Pandas, and Scikit-Learn.

• URL:
  https://­github.­com/­progressivis/­progressivis
• Contact:
  Jean Daniel Fekete

7.1.2 Cartolabe

• Name:
  Cartolabe
• 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, Cartolabe provides the user with a map of the thematics, authors, and articles. ML techniques are used for dimensionality reduction, cluster, and topic identification, visualization techniques are used for a scalable 2D representation of the results.

  Cartolabe has, in particular, been applied to the Grand Debat dataset (3M individual propositions from French Citizen, see https://cartolabe.fr/map/debat). The results were used to test both the scaling capabilities of Cartolabe and its flexibility to non-scientific and non-English corpora. We also added sub-map capabilities to display the result of a year/lab/word filtering as an online generated heatmap with only the filtered points to facilitate the exploration. Cartolabe has also been applied in 2020 to the COVID-19 Kaggle publication dataset (Cartolabe-COVID project) to explore these publications.

• URL:
  http://­www.­cartolabe.­fr/
• Publication:
  hal-02499006
• Contact:
  Philippe Caillou
• Participants:
  Philippe Caillou, Jean Daniel Fekete, Michèle Sebag, Anne-Catherine Letournel, Hande Gozukan
• Partners:
  LRI - Laboratoire de Recherche en Informatique, CNRS

7.1.3 SwimFlow

• Name:
  Visualization tool for swimming analytics
• Keywords:
  Data visualization, Video analysis
• Functional Description:
  SwimFlow contains a set of basic features for prototyping visualizations in motion coupled with a video.
• Release Contributions:
  Initial entry. This version includes restructured code designed by Ludovic David from the code initially written by Lijie Yao, PhD student in the Aviz team. It restructured the code to make it faster and more easily extensible.
• URL:
  https://­gitlab.­inria.­fr/­aviz/­swimflow
• Contact:
  Petra Isenberg

7.1.4 ParcoursVis

• Name:
  Visualization of Patient Pathways
• Keywords:
  Visualization, Health, Progressive visualization
• Scientific Description:
  We developed ParcoursVis, our Progressive Visual Analytics (PVA) tool to explore patients' care pathways at scale. Current tools to visualize temporal event sequences are restricted to datasets as large as a few thousand sequences to remain reactive. With ParcoursVis, we aim to visualize patients' care pathways stored in country-level databases, which can contain order of magnitudes higher of event sequences, at scale using a progressive architecture. PVA tools, instead of waiting for the whole computation to finish before rendering the final results, yield partial results each time the algorithm processes small chunks of data or iterations. This paradigm makes the tool reactive and quickens processes such as checking errors of a query.
• Functional Description:

  ParcoursVis allow extracting a subset of the nationwide database from CNAMTS, transforming the raw data into meaningful medical events, and visualizing it interactively at scale via a web interface.

  For the moment, ParcoursVis focuses on non-cancerous prostate adenoma. With this focus, our domain expert users extract meaningful high-level types of events (e.g., treatments and outcomes) that the patients undertake in their care pathways.

  Using a progressive visualization method, ParcoursVis visualizes in an aggregated manner the care pathways of tens of millions of patients treated with thousands of events over decades, several orders of magnitude more than existing interactive systems.

• URL:
  https://­www.­aviz.­fr/­Research/­ParcoursVis
• Contact:
  Jean Daniel Fekete

7.2.1 Prepare the ProgressiVis Toolkit for a Wider Distribution

Participants: Jean-Daniel Fekete [Inria, Paris-Saclay, correspondent], Christian Poli [Inria], Nivan Ferreira [Univ. Federal de Pernambuco, Brazil].

The ProgressiVis toolkit, implementing a Progressive Data Analysis and Visualization language and environment, has been under heavy development for many years. We have finished its packaging so it can be easily installed by partners who want to experiment with the toolkit and the concept it supports. A documentation is also available (in progress), as well as several graphical and interactive widgets to support its use in modern notebooks.

With the new book on Progressive Data Analysis published 2, we want to push the ProgressiVis toolkit as an advanced proof-of-concept of progressive system architecture.

7.3.1 Vispubdata.org

• Contributors:
  Petra Isenberg and Tobias Isenberg
• Description:
  The AVIZ members are making available for research a dataset of IEEE VIS publications at vispubdata.org. This dataset is actively being used for research and conference organization.
• Dataset DOI:
  10.1109/TVCG.2016.2615308,
• Project link:
  vispubdata.org/ and github.com/pisenberg/vispubdata/
• Publications:
  60
• Contact:
  email:petra.­isenberg@inria.­fr

7.3.2 Cartolabe

• Contributors:
  Jean-Daniel Fekete, Hande Gözükan, Philippe Caillou
• Description:
  Along with the Cartolabe system 53, we provide the results of our computations for the visualization of large text corpora.
• Dataset DOI:
  DOI:10.5281/zenodo.7432018, DOI:10.5281/zenodo.7858805, 10.5281/zenodo.7569091, 10.5281/zenodo.7323386, 10.5281/zenodo.7322976, 10.5281/zenodo.5718551, 10.5281/zenodo.5718514, 10.5281/zenodo.5718495.
• Project link:
  https://­zenodo.­org/­communities/­cartolabe/
• Publications:
  53
• Contact:
  email:Jean-Daniel.­Fekete@inria.­fr

7.3.3 State of Reproducibility Stamps for Visualization Research Papers

• Contributors:
  Tobias Isenberg
• Description:
  We have analyzed the contribution of visualization papers to the Graphics Replicability Stamp Initiative (GRSI, replicabilitystamp.org) and have made this data available on GitHub (github.com/tobiasisenberg/Visualization-Reproducibility). This dataset is planned to be updated at regular intervals and is also being integrated into the Vispubdata dataset (vispubdata.org).
• Dataset DOI:
  10.1109/BELIV64461.2024.00016
• Project link:
  github.com/tobiasisenberg/Visualization-Reproducibility
• Publications:
  35
• Contact:
  email:tobias.­isenberg@inria.­fr

7.3.4 Perceived readability dataset

• Contributors:
  Tobias Isenberg , Petra Isenberg , Tingying He and Anne-Flore Cabouat
• Description:
  We collected experimental data on the perceived readability of 9 visualizations while developing and validating a Perceived Readability Evaluation scale for Visualizations (PREVis). We will concatenate future studies' results to provide the research community with a growing dataset of visualizations at different levels of perceived readability.
• Dataset DOI:
  10.1109/TVCG.2024.3456318,
• Project link:
  osf.io/2g9qy, osf.io/2dcms
• Publications:
  8
• Contact:
  email:tobias.­isenberg@inria.­fr

7.3.5 IPCC WG1 Data

• Contributors:
  Jean-Daniel Fekete, Lu Ying
• Description:
  We created a github repository to provide all the information to reproduce the figures from the report of the IPCC WG1. The original data is available at https://­github.­com/­repro-ipcc/ but is not reproducible, lacking information, code, and data. We created the repository at https://­github.­com/­repro-ipcc/; it improves the reproducibility of the original repository by completing the data, code, and instructions to install the running environments for each figure and to run the code.
• Project link:
  Github repository
• Publications:
  38
• Contact:
  email:Jean-Daniel.­Fekete@inria.­fr

7.3.6 Open science

Aviz regularly shares full research material on the repository of the Center for Open Science to facilitate scrutiny, reuse, and replication:

• PREVis: Perceived Readability Evaluation for Visualizations 8 — OSF
• DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map 16 — OSF
• The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling 20 — OSF
• SpatialTouch: Exploring Spatial Data Visualizations in Cross-reality 28 — OSF
• In Space, No One (but AI) Can Hear You Scream 46 — OSF
• Design Characterization for Black-and-White Textures in Visualization 3 — OSF
• A Survey of Designs for Combined 2D+3D Visual Representations 4 — OSF
• Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage 13 — OSF, OSF
• The State of Reproducibility Stamps for Visualization Research Papers 35 — OSF
• Virtual Reality Inspection of Chromatin 3D and 2D Data 18 — OSF
• V-Mail: 3D-Enabled Correspondence about Spatial Data on (Almost) All Your Devices 19 — OSF
• MeTACAST: Target- and Context-aware Spatial Selection in VR 27 — OSF
• Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics 14 — OSF
• Eleven Years of Gender Data Visualization: A Step Towards More Inclusive Gender Representation 1 — OSF
• Micro Visualizations on a Smartwatch: Assessing Reading Performance While Walking 33 — https://osf.io/u78s6/
• Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays   15 — OSF
• Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups   36 — OSF
• Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos   6 — OSF
• A cognitive load approach to designing and evaluating data visualizations   47 — OSF

8.2.1 How to improve visualizations for fitness trackers

This figure shows four small data dashboard displayed on smartwaches. The dashboards vary in they way of representing calories burned, step count and active minutes.

We were specifically interested in understanding how people perceive visualizations on fitness trackers. In a first paper 36, we present results of a replication study on smartwatch visualizations with adults aged 65 and older. Smartwatches pose challenges to this population: fonts and visualizations are often small and meant to be seen at a glance. We replicated a study that investigated how visualization type and number of data points affect glanceable perception. We observe strong evidence of differences for participants aged 75 and older, sparking interesting questions regarding the study of visualization and older adults.

In a second project 33, we assessed how different walking trajectories (straight line, circular, and infinity) and speeds (2 km/h, 4 km/h, and 6 km/h) influence the accuracy and response time of participants reading micro visualizations on a smartwatch. We showed our participants common watch face micro visualizations including date, time, weather information, and four complications showing progress charts of fitness data. Our findings suggest that while walking trajectories did not significantly affect reading performance, overall walking activity, especially at high speeds, hurt reading accuracy and, to some extent, response time.

8.2.2 Embedded visualizations for sports

This figure highlights the wide diversity of visualizations that can be live-embedded in swimming videos.

In a second stream of research we investigated how to design visualizations to be embedded into sports videos or AR video streams. We specifically worked on shorter workshops and opinion pieces as first explorations into the topic of first-person visualizations for use during sports activities 32, 34. In addition, we published a full journal paper on our exploration of visualizations for swimming videos 6. Our work makes several contributions in identifying opportunities for visualizations to be added to swimming competition coverage but, most importantly, in identifying requirements and challenges for designing situated visualizations in motion. Our investigations include the analysis of a survey with swimming enthusiasts on their motion-related information needs, an ideation workshop to collect designs and elicit design challenges, the design of a technology probe that allows to create embedded visualizations in motion based on real data, and an evaluation with visualization designers that aimed to understand the benefits of designing directly on videos

10.1.1 Visits of international scientists

ParcoursVis

• Program:
  Post-doctoral funding
• Project acronym:
  ParcoursVis
• Project title:
  ParcoursVis: Visualization of Patient Pathways
• Duration:
  2022 – 2025.
• Coordinator:
  Jean-Daniel Fekete
• Participants in AVIZ:

  Participants: Jean-Daniel Fekete, Mickaël Sereno, Sungbok Shin.

• Other partners:
  Health-Data-Hub
• Abstract:
  The Aviz Team and the Health-Data-Hub (French Social Security) are interested in evaluating patient pathways for treatments. We are currently working on two main projects: (1) ParcoursVis and (2) ParcoursUrge. In ParcoursVis, doctors want to understand the differences between how treatments are de facto given around France and how they align with the official recommendations. In ParcoursUrge, we visualize the events that take place in the Emergency services of the Parisian hospitals to help doctors and hospitals optimize patient management. Both ParcoursVis and ParcoursUrge projects present several challenges, such as finding methodologies to effectively process large-scale data (multi-million patients with hundreds or thousands of events per patient). They transform raw data collected for prescription reimbursement or monitoring into an effective visualization. To visualize and interact with this high volume of data, we rely on Progressive Data Analysis. We visualize millions of patient events by aggregating them on the fly by "chunks", groups of 10,000 or so patients, iteratively. At each iteration, we update the visualization that quickly converges to the final result. This method allows aggregating and visualizing billions of patients with no noticeable delays, allowing smooth interactions at any time. We apply it to improve health at the country or regional level, a scale that was impossible to handle before.

URGE

• Program:
  Inria-AP-HP Funding
• Project acronym:
  URGE
• Project title:
  URGE: Analysing and optimizing emergency patient pathways
• Duration:
  2022 – 2026.
• Coordinator:
  Xavier Leroy (Inria) and Youri Yordanov (AP-HP, Inserm, Sorbonne Université)
• Participants in AVIZ:

  Participants: Jean-Daniel Fekete, Mickaël Sereno, Ambre Assor.

• Other partners:
  Hôpital Saint Antoine, Sorbonne Université
• Abstract:
  For more than 15 years, emergency departments have been facing a steady increase in their number of patients, leading to long periods of crowding, defined as the moments when health care demand exceeds the department's capacity to provide quality care within a reasonable time. The URGE project aims at developing mathematical analysis and numerical simulation tools for flows analysis of the emergency departments. Our objectives are to assess the performances of different organizations, to evaluate the impact of their evolutions and optimizations, starting from the triage step and until the end of the patient journey, in a risk-free environment. This project brings together teams from the AP-HP emergency departments, the Tropical team (Inria Saclay - CMAP, École polytechnique), the Aviz team (Inria Saclay - Université Paris-Saclay), the Dyogene team (Inria Paris - ENS), the "Observatoire des soins non programmés d'île de France" and the "plateforme de recherche Est Parisien" (AP-HP).

EquityAnalytics

• Program:
  Inria AeX
• Project acronym:
  EquityAnalytics
• Project title:
  EquityAnalytics
• Duration:
  2022 – 2024.
• Coordinator:
  Petra Isenberg
• Participants in AVIZ:

  Participants: Petra Isenberg, Florent Cabric.

• Other partners:
  PayAnalytics
• Abstract:
  The goal of this project is to explore, develop, and test pay equity visual analytics solutions in order to give decision makers the power to eradicate adjusted gender pay gaps. We will develop new visual analytics solutions that summarize pay structures in a manner that clearly shows the changes that should be made in order for the pay structure to be fair and rational and that allows managers to evaluate recommended action plans, make informed decisions and monitor progress using a visual interactive user interface. We will collaborate with a startup company called PayAnalytics that provides access to real data and use cases.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Jean-Daniel Fekete
  • Improving IPCC Data Visualization with Traceability, Keynote for the 2024 Pernambuco Seminar on visualization and sustainability, Recife, Brazil, Oct. 21, 2024
  • Presentation of the book on Progressive Data Analysis at the EuroVA session on Progressive Data Analysis at EuroVA 2024, Odense, DK, May 27th, 2024
  • Scalability in Visualization and Visual Analytics with Progressive Data Analysis, Keynote for AVI 2024, Arenzano (Genoa), Italy, Jun. 5, 2024
  • Visualisation de données : quelques avancées récentes, Keynote for the Chimiométrie 2024 conference, Nantes, Feb. 27, 2024
• Petra Isenberg
  • Bringing Data to Life: Embedded Visualizations for Pervasive and Mobile Data Exploration. Keynote at CGVC 2024
  • Visualization for Mobile Devices and Embedded Experiences, Seminar University of Bremen, Germany
• Tobias Isenberg
  • Visualization of Species Distributions based on Social Media Data and a Couple of Surprises and Insights. Keynote at VCBM (September 2024)
  • Thoughts on how to Effectively Explore Visual Representations in Mixed Reality, University of Stuttgart, Germany (April 2024)

11.1.5 Leadership within the scientific community

• Jean-Daniel Fekete: Co-Chair of the IEEE VIS Area Curation Committee
• Petra Isenberg: member of the steering committee of the BELIV Workshop on Evaluation and Beyond—Methological Approaches for Visualization, vice-chair of the IEEE VIS steering committee
• Tobias Isenberg: member of the steering committee of the BELIV Workshop on Evaluation and Beyond—Methological Approaches for Visualization
• Florent Cabric (vice-chair) and Petra Isenberg (treasurer): members of ACM SIGCHI Paris Local Chapter.
• Florent Cabric: co-chair of joint Groupe Travail (GT) VISU for the Groupements De Recherche (GdR) Informatique Géométrique et graphique, la Réalité virtuelle et la Visualisation (IG-RV) and Interaction Humain-Machine (IHM).

11.1.6 Scientific expertise

• Petra Isenberg: reviewer for multiple grant organizations (FWF, FWO, NSERC, Mitacs), hiring committees, promotion committees, and the Graphics Replicability Stamp committee
• Tobias Isenberg: member of ANR grant evaluation committee “Interaction Robotique” (CE33) for ANR’s AAPG 2024; reviewer for grant application (FWF)

11.2.1 Teaching

• Master: Jean-Daniel Fekete , Visual Data Analysis at the Parisian Master of Research in Computer Science with Julien Tierny. 25 hours. Joint Master with Univ. Paris-Cité, ENS Ulm, Univ. Paris-Saclay, Institut Polytechnique de Paris.
• Crash course: Jean-Daniel Fekete , on HCI and Visualization, 6h, to IRT SystemX
• Master: Petra Isenberg, Katerina Batziakoudi, “Interactive Information Visualization”, 36.5h equivalent TD (total, co-taught with Anastasia Bezerianos), M1/M2, Université Paris-Saclay, France.
• Master: Petra Isenberg , “Visual Analytics”, 60h equivalent TD (co-taught with Natkamon Tovanich), M2, Centrale Supelec.
• Licence: Tobias Isenberg , “Introduction to Computer Graphics”, 18h en équivalent TD, L3, Polytech Paris-Saclay, France.
• Master: Tobias Isenberg , Tingying He , “Data Visualization”, 36h en équivalent TD, M2, CentraleSupélec, France.
• Master: Frédéric Vernier , class and lab class. “Information visualization”, 24h en équivalent TD, M2 ISC, Faculté des sciences d'Orsay. Université Paris-Saclay, France.
• Master: Frédéric Vernier , class and lab class. “Web development with node.js”, 21h en équivalent TD, M2 HCI, Faculté des sciences d'Orsay. Université Paris-Saclay, France.
• Licence: Frédéric Vernier , class and lab class. “Introduction to Computer Graphics”, 42h en équivalent TD, L1-MI, Faculté des sciences d'Orsay. Université Paris-Saclay, France.
• Licence: Frédéric Vernier , class and lab class. “Advanced Computer Graphics”, 36h en équivalent TD, L2-MI, Faculté des sciences d'Orsay. Université Paris-Saclay, France.
• Licence: Frédéric Vernier , class and lab class. “Web development”, 42h en équivalent TD, L3 computer sciences, Faculté des sciences d'Orsay. Univ. Paris-Saclay, France.
• BUT Informatique: Florent Cabric , class and lab class. “Analyse,’’ 22.5 heures en équivalent TD, 2nd year of BUT, IUT d’Orsay. Université Paris-Saclay, France.
• BUT Informatique: Florent Cabric , lab class. “Introduction aux bases de données et SQL,’’ 39 heures en équivalent TD, 1st year of BUT, IUT d’Orsay. Université Paris-Saclay, France.
• BUT Informatique: Florent Cabric , project supervision. “Création d'une base de données,’’ 10 heures en équivalent TD, 1st year of BUT, IUT d’Orsay. Université Paris-Saclay, France.

11.2.2 Supervision

• PhD in progress: Katerina Batziakoudi, Visualization of budget and accounting data, supervised by Jean-Daniel Fekete .
• PhD in progress: Anne-Flore Cabouat , Readability of Visualizations, Univ. Paris-Saclay, supervised by Petra Isenberg.
• PhD in progress: Lixiang Zhao, Interaction Techniques for Spatial 3D data in VR and Mixed Reality, Xi’an Jiaotong-Liverpool Univ. (China), co-supervised by Tobias Isenberg .
• PhD in progress: Yucheng Lu, Hybrid Scientific Data Exploration, Univ. Paris-Saclay, defense planned for December 2026, co-supervised by Tobias Isenberg .
• PhD in progress and visiting PhD student: Tobias Rau , Interactive AR/VR Visualization of Molecular Simulation Data, Univ. Stuttgart (Germany), co-supervised by Tobias Isenberg .
• PhD in progress: Deng Luo, Abstraction of Visual Representations for DNA Nanotechnology and Protein Data, KAUST (Saudi-Arabia), co-supervised by Tobias Isenberg .
• PhD completed: Tingying He , A Design Space for Visual Data Mapping for Low-Color Displays, Univ. Paris-Saclay, defended September 2024, co-supervised by Tobias Isenberg .
• Post-doctoral fellow: Ambre Assor , Emergency department patients pathways and care optimization, supervised by Jean-Daniel Fekete .
• Post-doctoral fellow: Sungbok Shin , Progressive Visualization of Patient Pathways at Scale, supervised by Jean-Daniel Fekete .
• Post-doctoral fellow: Florent Cabric , Gender data visualization for reducing gender pay gap, supervised by Petra Isenberg .
• Masters completed: Anne-Flore Cabouat , A Validated Scale for Measuring the Readability of Visualizations, Univ. Paris Cité, supervised by Tobias Isenberg and Petra Isenberg .
• Masters completed: Zihan Lu , A Design Space of Semantically-Resonant Patterns, Univ. Paris-Saclay, supervised by Tobias Isenberg and Tingying He .
• Visiting PhD Student: Karelia Alexandra Vilca Salinas , Visual analytics for crime analysis, co-supervised by Jean-Daniel Fekete .
• Visiting PhD Student: Lu Ying , Improve the public understanding of the information gathered by IPCC, co-supervised by Jean-Daniel Fekete .
• Visiting PhD Student: Filip Opálený , Interaction with Chromatin Data Representations, co-supervised by Tobias Isenberg .

11.2.3 Juries

• Jean-Daniel Fekete: Alex Ulmer, PhD , “Progressive Visual Analytics for Temporal Data”, Technische Universität Darmstadt, Nov. 19, 2024
• Petra Isenberg: PhD thesis examiner, Alexandre Battut, Université Paris-Saclay, jury president
• Petra Isenberg: PhD thesis reporter, Bradley Rey, University of British Columbia
• Petra Isenberg: HDR committee member, Dr. Benjamin Bach, University of Bordeaux/Inria
• Petra Isenberg: member of comité de suivi for Alan Gany, Université Paris-Saclay
• Petra Isenberg: member of comité de suivi for Yasaman Marandi, Université Paris-Saclay
• Petra Isenberg: member of comité de suivi for Anna Offenwanger, Université Paris-Saclay
• Tobias Isenberg: member of PhD jury for Charlotte Godard, Université PSL/École Normale Supérieure, France
• Tobias Isenberg: president of HdR jury for Dr. Mohamed El Beheiry, Institut Pasteur, France
• Tobias Isenberg: member of comité de suivi for Vincent Bonczak, Université Paris-Saclay

International journals

• 7 articleA.Ambre Assor, A.Arnaud Prouzeau, P.Pierre Dragicevic and M.Martin Hachet. Augmented-Reality Waste Accumulation Visualizations.ACM Journal on Computing and Sustainable Societies211January 2024, 1-29HALDOI
• 8 articleA.-F.Anne-Flore Cabouat, T.Tingying He, P.Petra Isenberg and T.Tobias Isenberg. PREVis: Perceived Readability Evaluation for Visualizations.IEEE Transactions on Visualization and Computer Graphics311January 2025, 1083–1093HALDOIback to textback to textback to textback to textback to text
• 9 articleF.Florent Cabric, M.Margrét Vilborg Bjarnadóttir, M.Meng Ling, G.Guðbjörg Linda Rafnsdóttir and P.Petra Isenberg. Eleven Years of Gender Data Visualization: A Step Towards More Inclusive Gender Representation.IEEE Transactions on Visualization and Computer Graphics301January 2024, 316-326HALDOI
• 10 articleX.Xin Chen, Y.Yunhai Wang, H.Huaiwei Bao, K.Kecheng Lu, J.Jaemin Jo, C.-W.Chi-Wing Fu and J.-D.Jean-Daniel Fekete. Visualization-Driven Illumination for Density Plots.IEEE Transactions on Visualization and Computer Graphics312January 2025, 1631-1644HALDOI
• 11 articleT.Tingying He, Y.Yuanyang Zhong, P.Petra Isenberg and T.Tobias Isenberg. Design Characterization for Black-and-White Textures in Visualization.IEEE Transactions on Visualization and Computer Graphics301January 2024, 1019--1029HALDOI
• 12 articleJ.Jiayi Hong, R.Rostyslav Hnatyshyn, E. A.Ebrar A. D. Santos, R.Ross Maciejewski and T.Tobias Isenberg. A Survey of Designs for Combined 2D+3D Visual Representations.IEEE Transactions on Visualization and Computer Graphics306June 2024, 2888–2902HALDOI
• 13 articleJ.Jiayi Hong, R.Ross Maciejewski, A.Alain Trubuil and T.Tobias Isenberg. Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage.IEEE Transactions on Visualization and Computer Graphics304April 2024, 1956–1969HALDOIback to textback to text
• 14 articleG. M.Gabriela Molina León, A.Anastasia Bezerianos, O.Olivier Gladin and P.Petra Isenberg. Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics.IEEE Transactions on Visualization and Computer Graphics311January 2025, 941--951HALDOIback to textback to textback to text
• 15 articleG. M.Gabriela Molina León, P.Petra Isenberg and A.Andreas Breiter. Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays.IEEE Transactions on Visualization and Computer Graphics302February 2024, 1624-1637HALDOIback to text
• 16 articleD.Deng Luo, Z.Zainab Alsuwaykit, D.Dawar Khan, O.Ondřej Strnad, T.Tobias Isenberg and I.Ivan Viola. DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map.IEEE Transactions on Visualization and Computer Graphics311January 2025, 558–568HALDOIback to text
• 17 articleD.Deng Luo, Z.Zainab Alsuwaykit, D.Dawar Khan, O.Ondřej Strnad, T.Tobias Isenberg and I.Ivan Viola. Errata to “DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map”.IEEE Transactions on Visualization and Computer Graphics312February 2025, 1645-1646HALDOI
• 18 articleE.Elena Molina, D.David Kouřil, T.Tobias Isenberg, B.Barbora Kozlíková and P.Pere‐pau Vázquez. Virtual reality inspection of chromatin 3D and 2D data.Computers and Graphics124November 2024, article no. 104059, 11 pagesHALDOIback to text
• 19 articleJ. W.Jung Who Nam, T.Tobias Isenberg and D.Daniel Keefe. V-Mail: 3D-Enabled Correspondence about Spatial Data on (Almost) All Your Devices.IEEE Transactions on Visualization and Computer Graphics304April 2024, 1853–1867HALDOIback to textback to text
• 20 articleH.Hana Pokojná, T.Tobias Isenberg, S.Stefan Bruckner, B.Barbora Kozlíková and L.Laura Garrison. The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling.IEEE Transactions on Visualization and Computer Graphics311January 2025, 371–381HALDOIback to textback to text
• 21 articleT.Tobias Rau, M.Maurice Koch, N.Nelusa Pathmanathan, T.Tobias Isenberg, D.Daniel Weiskopf, M.Michael Sedlmair, A.Andreas Köhn and K.Kuno Kurzhals. Understanding Collaborative Learning of Molecular Structures in AR With Eye Tracking.IEEE Computer Graphics and Applications446November 2024, 39-51HALDOI
• 22 articleG.Gaëlle Richer, A.Alexis Pister, M.Moataz Abdelaal, J.-D.Jean-Daniel Fekete, M.Michael Sedlmair and D.Daniel Weiskopf. Scalability in Visualization.IEEE Transactions on Visualization and Computer Graphics307January 2024, 3314 - 3330HALDOI
• 23 articleA.Alex Ulmer, M.Marco Angelini, J.-D.Jean-Daniel Fekete, J.Jörn Kohlhammer and T.Thorsten May. A Survey on Progressive Visualization.IEEE Transactions on Visualization and Computer Graphics309January 2024, 6447 - 6467HALDOI
• 24 articleL.Lijie Yao, F.Federica Bucchieri, V.Victoria Mcarthur, A.Anastasia Bezerianos and P.Petra Isenberg. User Experience of Visualizations in Motion: A Case Study and Design Considerations.IEEE Transactions on Visualization and Computer Graphics311January 2025, 174--184HALDOI
• 25 articleL.Lijie Yao. Situated Visualization in Motion.IEEE Computer Graphics and Applications446January 2025, 142-150HALDOI
• 26 articleL.Lijie Yao, R.Romain Vuillemot, A.Anastasia Bezerianos and P.Petra Isenberg. Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos.IEEE Transactions on Visualization and Computer Graphics303March 2024, 1821-1836HALDOI
• 27 articleL.Lixiang Zhao, T.Tobias Isenberg, F.Fuqi Xie, H.-N.Hai-Ning Liang and L.Lingyun Yu. MeTACAST: Target-and Context-aware Spatial Selection in VR.IEEE Transactions on Visualization and Computer Graphics301January 2024, 480--494HALDOIback to textback to text
• 28 articleL.Lixiang Zhao, T.Tobias Isenberg, F.Fuqi Xie, H.-N.Hai-Ning Liang and L.Lingyun Yu. SpatialTouch: Exploring Spatial Data Visualizations in Cross-reality.IEEE Transactions on Visualization and Computer Graphics311January 2025, 897–907HALDOIback to textback to text

International peer-reviewed conferences

• 29 inproceedingsK.Katerina Batziakoudi, F.Florent Cabric, S.Stéphanie Rey and J.-D.Jean-Daniel Fekete. Designing Visualizations for Enhancing Carbon Numeracy.IEEE VIS 2024 Workshop on Visualization for Climate Action and SustainabilityVirtual, United StatesOctober 2024HALback to text
• 30 inproceedingsA.-F.Anne-Flore Cabouat, T.Tingying He, F.Florent Cabric, T.Tobias Isenberg and P.Petra Isenberg. Position paper: A case to study the relationship between data visualization readability and visualization literacy.ACM CHI'24CHI 2024 - Workshop Toward a More Comprehensive Understanding of Visualization LiteracyO'ahu (Honolulu), United StatesMay 2024HALback to text
• 31 inproceedingsF.Florent Cabric and J.-D.Jean-Daniel Fekete. Plea for a Hybride IHM Conference.IHM 2024 - 35e Conférence Internationale Francophone sur l'Interaction Humain-MachineIHM’24 : Actes étendus de la 35ème conférence Francophone sur l’Interaction Humain-MachineParis, FranceMarch 2024HAL
• 32 inproceedingsA.Ahmed Elshabasi, L.Lijie Yao, P.Petra Isenberg, C.Charles Perin and W.Wesley Willett. Collecting Information Needs for Egocentric Visualizations while Running.IEEE VIS Workshop on First-Person Visualizations for Outdoor Physical Activities: Challenges and OpportunitiesSt. Pete Beach (Florida), United StatesOctober 2024HALDOIback to text
• 33 inproceedingsF.Fairouz Grioui, T.Tanja Blascheck, L.Lijie Yao and P.Petra Isenberg. Micro Visualizations on a Smartwatch: Assessing Reading Performance While Walking.Short Paper Proceedings of the IEEE Conference on Visualization and Visual Analytics (VIS)IEEE Conference on Visualization and Visual Analytics (VIS)Los Alamitos, CA, USA, United StatesOctober 2024HALDOIback to textback to text
• 34 inproceedingsP.Petra Isenberg, L.Lionel Reveret and R.Romain Vuillemot. HomeOlympics: Turning Everyday Outdoor Physical Activities into Olympic-Style Experience.Proceedings of the IEEE VIS Workshop on First-Person Visualizations for Outdoor Physical Activities: Challenges and OpportunitiesIEEE VIS Workshop on First-Person Visualizations for Outdoor Physical Activities: Challenges and OpportunitiesVirtual, Unknown RegionOctober 2024HALback to text
• 35 inproceedingsT.Tobias Isenberg. The State of Reproducibility Stamps for Visualization Research Papers.Proceedings of BELIVProceedings of BELIV Workshop 2024 - evaluation and BEyond - methodoLogIcal approaches for VisualizationSt. Pete Beach, Florida, United StatesOctober 2024, 97–105HALDOIback to textback to text
• 36 inproceedingsZ.Zack While, T.Tanja Blascheck, Y.Yujie Gong, P.Petra Isenberg and A.Ali Sarvghad. Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups.Proceedings of the Conference on Human Factors in Computing Systems (CHI)CHI 2024 - Conference on Human Factors in Computing SystemsHonolulu, United StatesACMMay 2024, 987:1-987:17HALDOIback to textback to textback to text
• 37 inproceedingsZ.Zack While, H.Henry Wheeler-Klainberg, T.Tanja Blascheck, P.Petra Isenberg and A.Ali Sarvghad. Toward Understanding the Experiences of People in Late Adulthood with Embedded Information Displays in the Home.VIS 2024 - IEEE VIS 1st Workshop on Accessible Data VisualizationVirtual, United StatesarXiv2024HALDOI
• 38 inproceedingsL.Lu Ying, Y.Yingcai Wu and J.-D.Jean-Daniel Fekete. Exploring the Reproducibility for Visualization Figures in Climate Change Report.IEEE VIS 2024 Workshop on Visualization for Climate Action and SustainabilityVirtual, United StatesOctober 2024HALback to textback to text

Scientific books

• 39 bookJ.-D.Jean-Daniel Fekete, D.Danyel Fisher and M.Michael Sedlmair, eds. Progressive Data Analysis: Roadmap and Research Agenda.EurographicsNovember 2024, 231HALDOI

Scientific book chapters

• 40 inbookJ.-D.Jean-Daniel Fekete. Data Structures and Algorithms for Progressive Data Analysis.Progressive Data Analysis: Roadmap and Research AgendaEurographicsNovember 2024, 49-68HAL
• 41 inbookG.Gaëlle Richer, J.-D.Jean-Daniel Fekete and M.Michael Sedlmair. Evaluation for Progressive Data Analysis.Progressive Data Analysis: Roadmap and Research AgendaEurographicsNovember 2024, 150-170HAL
• 42 inbookA.Anna Vilanova, M.Marco Angelini, S. K.Sriram Karthik Badam and J.-D.Jean-Daniel Fekete. Uncertainty and Quality for Progressive Data Analysis.Progressive Data Analysis: Roadmap and Research AgendaEurographicsNovember 2024, 92-107HAL

Edition (books, proceedings, special issue of a journal)

• 43 proceedingsEighth Workshop on Human-In-the-Loop Data Analytics (HILDA).SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of DataACMJune 2024, 657-658HALDOI

Reports & preprints

• 44 miscK.Katerina Batziakoudi, F.Florent Cabric, S.Stéphanie Rey and J.-D.Jean-Daniel Fekete. Lost in Magnitudes: Exploring the Design Space for Visualizing Data with Large Value Ranges.April 2024HAL

Other scientific publications

• 45 inproceedingsK.Katerina Batziakoudi, F.Florent Cabric, S.S Rey and J.-D.Jean-Daniel Fekete. A Design Space for Static Visualizations with Several Orders of Magnitude.EuroVis 2024 -Odense, DenmarkMay 2024HAL
• 46 inproceedingsM.Mathis Brossier, A.Alexander Bock, K.Konrad Schönborn, T.Tobias Isenberg, A.Anders Ynnerman and L.Lonni Besançon. In space, no one (but AI) can hear you scream.Posters of VIS 2024 - IEEE VisualizationSt. Pete Beach, Florida, United StatesOctober 2024HALback to text
• 47 thesisA.-F.Anne-Flore Cabouat. A cognitive load approach to designing and evaluating data visualizations.Universite Paris CiteJune 2024, 31HALDOIback to text
• 48 inproceedingsD.Deng Luo, Z.Zainab Alsuwaykit, D.Dawar Khan, O.Ondřej Strnad, T.Tobias Isenberg and I.Ivan Viola. DiffFit: Differentiable Fitting of Molecule Structures to a Cryo-EM Map.Posters of ICML 2024 - Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and SimulatorsVienna, AustriaJuly 2024, article no. 32, 6 pagesHAL
• 49 inproceedingsK.Karelia Salinas, J.-D.Jean-Daniel Fekete and L. G.Luis Gustavo Nonato. Navigating Multi-Attribute Spatial Data Through Layer Toggling and Visibility-Preserving Lenses.Posters of VIS 2024 - IEEE VisualizationVirtual, United StatesOctober 2024HAL