EDBT 2026 Demo / reviewers in the wild / expert
Oujing Liu
dblp:344/8508
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-3038-2196ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 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% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
computational notebook |
0.7 | 1 | 2023 | Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration · CHI 2023 |
Visualization and visual analytics › visual communication
presentation generation |
0.7 | 1 | 2023 | Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration · CHI 2023 |
Human-AI interaction
human-AI collaboration |
0.7 | 1 | 2023 | Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration · CHI 2023 |
Methods — techniques the papers use, named apart from their topics
natural language processing · 1.3large language model · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI CollaborationabstractData scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks. Fengjie Wang, Xuye Liu, Oujing Liu, Ali Neshati, Tengfei Ma 0001, Min Zhu 0005, Jian Zhao 0010 |
CHI | 3 |