VLDB 2026 Research / reviewers in the wild / expert
Wonyoung So
dblp:276/5742
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-4867-3429ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
medical visualization |
0.5 | 1 | 2021 | Humane Visual AI: Telling the Stories Behind a Medical Condition · IEEE Trans. Vis. Comput. Graph. 2021 |
Visualization and visual analytics › data storytelling
narrative visualization |
0.5 | 1 | 2021 | Humane Visual AI: Telling the Stories Behind a Medical Condition · IEEE Trans. Vis. Comput. Graph. 2021 |
Web and social media mining
social media analysis |
0.1 | 1 | 2021 | Humane Visual AI: Telling the Stories Behind a Medical Condition · IEEE Trans. Vis. Comput. Graph. 2021 |
Methods — techniques the papers use, named apart from their topics
probabilistic modeling · 1.5deep learning · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Humane Visual AI: Telling the Stories Behind a Medical ConditionabstractA biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations. Wonyoung So, Edyta Paulina Bogucka, Sanja Scepanovic, Sagar Joglekar 0001, Ke Zhou 0003, Daniele Quercia |
IEEE Trans. Vis. Comput. Graph. | 1 |