1. Track C
1.1. Immersive Analytics
1.1.1. 1004: Collaborative 3D Data Exploration Using Augmented Reality as a Support
1.1.1.1. SciVis
1.1.2. 1014: Designing Efficient Immersive Analytics Environments for Spatio-Temporal Data
1.1.3. Panelists:
1.1.3.1. Tim Dwyer
1.1.3.2. Manuela Waldner
1.2. Networks/Graphs
1.2.1. 1000: Application of Insights from Tree Visualization Design Studies towards a Novel Data- and Task-Based Tree Visualization Recommendation System
1.2.2. 1016: High-level specification language for domain-aware visualization of computing graphs
1.2.3. Panelists:
1.2.3.1. Tim Dwyer
1.2.3.2. Manuela Waldner
1.3. Design
1.3.1. 1010: In Search of Meaning: Exploring and Designing Infovis Tools for Art History Research
1.3.2. 1015: Domain Characterization and Evaluation in Problem-Driven Visualization Research
1.3.3. Panelists:
1.3.3.1. Tim Dwyer
1.3.3.2. Manuela Waldner
1.4. Assigned DC chair
1.4.1. Wolfgang
2. Track A
2.1. Uncertainty
2.1.1. 1023: In-Situ Steering for Progressive Visual Analytics
2.1.2. 1013: Visual Analytics for Analyzing and Contextualizing Smartphone-Sensed Health Data
2.1.3. 1007: Statistical Analysis and Uncertainty Visualization of Topological Descriptors
2.1.3.1. SciVis
2.1.4. 1008: Developing an uncertainty visualisation framework: An investigation into how aesthetic factors may enhance people's perceptions of uncertainty when making decisions from real-world data visualisations.
2.1.4.1. Moved this from design
2.1.5. Panelists:
2.1.5.1. Jessica Hullman
2.1.5.2. Guoning Chen
2.2. Perception
2.2.1. 1024: Revealing Perceptual Proxies in Data Visualization
2.2.2. 1012: Using Visual embellishment in aiding design of data visualisations for non-experts
2.2.3. 1001: Constructing Frameworks for Task-Optimized Visualizations
2.2.4. Panelists:
2.2.4.1. Jessica Hullman
2.2.4.2. Guoning Chen
2.3. Assigned DC chair
2.3.1. Danielle
3. Track B
3.1. Explainability
3.1.1. 1009: Expose to explain, explain to expose.
3.1.2. 1022: Bridging the Gap Between Exploration and Explanation: Combining Text and Visualizations for Dissemination of Data Analysis Results
3.1.3. Panelists:
3.1.3.1. Niklas Elmqvist
3.1.3.2. Ingrid Hotz
3.2. ML / AI
3.2.1. 1006: Interpreting Black-box Machine Learning Models By Visually Exploring High-Fidelity Surrogate Rules
3.2.2. 1018: Transparency and Interpretability for Deep Reinforcement Learning: Understanding Decision in Navigation Tasks
3.2.3. 1003: Automating Animated Transitions in Statistical Graphics
3.2.4. 1005: Visual Assistance in Model-Based Clinical Decision Support
3.2.5. Panelists:
3.2.5.1. Niklas Elmqvist
3.2.5.2. Ingrid Hotz
3.3. Assigned DC chair
3.3.1. Bei