Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yannis Mantzouratos
dblp:164/8308
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Robot manipulation · 77% Motion planning and robot control · 23% | |
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › robot design
modular robot design |
0.2 | 1 | 2015 | On embeddability of modular robot designs · ICRA 2015 |
Graph algorithms and graph theory
graph embedding |
0.2 | 1 | 2015 | On embeddability of modular robot designs · ICRA 2015 |
Robotics › Motion planning and robot control › robot kinematics
kinematic constraints |
0.1 | 1 | 2015 | On embeddability of modular robot designs · ICRA 2015 |
Methods — techniques the papers use, named apart from their topics
maximum cardinality matching · 0.4dynamic programming · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | On embeddability of modular robot designsabstractWe address the problem of detecting embeddability of modular robots: namely, to decide automatically whether a given modular robot design can simulate the functionality of a seemingly different design. To that end, we introduce a novel graph representation for modular robots and formalize the notion of embedding through topological and kinematic conditions. Based on that, we develop an algorithm that decides embeddability when the two involved designs have tree topologies. Our algorithm performs two passes and involves dynamic programming and maximum cardinality matching. We demonstrate our approach on real modular robots and show that we can detect embeddability of complex designs efficiently. Yannis Mantzouratos, Tarik Tosun, Sanjeev Khanna, Mark Yim |
ICRA | 1 |