Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeremia Geiger
dblp:334/4672
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, 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.
| Artificial intelligence
1 paper |
Optimization for machine learning · 100% | |
| Computer graphics and multimedia
1 paper |
Computational fabrication · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Optimization for machine learning
combinatorial optimization |
0.7 | 1 | 2023 | Automating Rigid Origami Design · IJCAI 2023 |
Computational fabrication
origami design |
0.7 | 1 | 2023 | Automating Rigid Origami Design · IJCAI 2023 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Automating Rigid Origami DesignabstractRigid origami has shown potential in large diversity of practical applications. However, current rigid origami crease pattern design mostly relies on known tessellations. This strongly limits the diversity and novelty of patterns that can be created. In this work, we build upon the recently developed principle of three units method to formulate rigid origami design as a discrete optimization problem, the rigid origami game. Our implementation allows for a simple definition of diverse objectives and thereby expands the potential of rigid origami further to optimized, application-specific crease patterns. We showcase the flexibility of our formulation through use of a diverse set of search methods in several illustrative case studies. We are not only able to construct various patterns that approximate given target shapes, but to also specify abstract, function-based rewards which result in novel, foldable and functional designs for everyday objects. Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer |
IJCAI | 1 |