Demo Data from EDBT 2026 / reviewers in the wild / expert
Akiko Murakami
dblp:59/3561
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Artificial intelligence
1 paper |
Robot manipulation · 50% Trustworthy machine learning · 50% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
safe human-robot interaction |
0.3 | 1 | 2025 | A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
foundation models · 0.9foundation model · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled RoboticsabstractRecent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to handle diverse tasks thus creates new opportunities to replace human labor. However, unlike general foundation models, FMRs interact with the physical world, where their actions directly affect the safety of humans and surrounding objects, requiring careful deployment and control. Based on this proposition, our survey comprehensively summarizes robot control approaches to mitigate physical risks by covering all the lifespan of FMRs ranging from pre-deployment to post-accident stage. Specifically, we broadly divide the timeline into the following three phases: (1) pre-deployment phase, (2) pre-incident phase, and (3) post-incident phase. Throughout this survey, we find that there is much room to study (i) pre-incident risk mitigation strategies, (ii) research that assumes physical interaction with humans, and (iii) essential issues of foundation models themselves. We hope that this survey will be a milestone in providing a high-resolution analysis of the physical risks of FMRs and their control, contributing to the realization of a good human-robot relationship. Takeshi Kojima, Yaonan Zhu, Yusuke Iwasawa, Toshinori Kitamura, Gang Yan 0003, Shu Morikuni, Ryosuke Takanami, Alfredo Solano, Tatsuya Matsushima, Akiko Murakami, Yutaka Matsuo |
IJCAI | 10 |
| 2011 | Multi-topical Discussion Summarization Using Structured Lexical Chains and Cue Words
Jun Hatori, Akiko Murakami, Jun'ichi Tsujii |
CICLing (2) | 2 |
| 2004 | Term Aggregation: Mining Synonymous Expressions using Personal Stylistic Variations
Akiko Murakami, Tetsuya Nasukawa |
COLING | 1 |