Information Visualization

... visual perception

... data models

... visualization techniques

... applications

... evaluation
Professor
Fanny Chevalier ( http://fannychevalier.net) Inria / U of T email: fanny [at] dgp.toronto.eduGrading scheme
- paper presentation: 40% (see details )
- implementation and demo of a small self-contained idea on information visualization: 40% (due by 5 Dec. 2016). Mid-term evaluation of the project is worth 10 of the 40%.
- project report (4-6 pages, references do not count toward page limit): 20% (due by 12 Dec. 2016).
Duration
The class will meet once a week, Mondays 4-6PM inSchedule
Week 1 | Welcome, Introduction ( slides ) |
Week 2 | Visual Perception / Prospective Projects ( slides ) |
Week 3 | Data Models ( slides ) + paper presentations (4) |
Week 4 | Student paper presentations (6) |
Week 5 | Holiday |
Week 6 | Guest Speaker: Christopher Collins (UOIT) Text Visualization (slides) |
Week 7 | Mid-term Review |
Week 8 | Guest Speaker: Richard Brath (Uncharted) 25 years of industry infovis: big data, analytics, realtime, and problems not addressed in research (slides: part1 part2) |
Week 9 | Students paper presentation |
Week 10 | Guest Speaker: Justin Matejka (Autodesk Research) Visualization and Aesthetics |
Week 11 | Guest Speaker: Isabel Mereilles (OCAD University) Information design: Technologies of production and readership in news media (slides) |
Week 12 | Students paper presentation |
Week 13 | Final presentations / Wrap Up |
Paper presentations
List of papers and instructions are on the PAPER PRESENTATION PAGEPotential projects
List of project ideas, schedule and instructions are on the PROJECT PAGE- Visualisation of Photoshop layers / pixel history
- Interactive visualization for nutrition (see e.g. Rethink the food label)
- Visualizing music (see e.g. TSO visualizations )
- Animated transition of the internet
- Expressing and visualizing body pain (see BodyDiagrams )
- (Sketch-based) Tool for authoring animations for visualization (see Skuid, Draco)
- Visualizing algorithms / machine learning (see Visual introduction to machine learning)
- Visualization literacy (at school)
- VAST Challenges 2016, previous years