VLDB 2026 Research / reviewers in the wild / expert
Laura Del Rio Martin
dblp:397/3782
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0002-7812-3161ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing
GPU computing |
0.9 | 1 | 2025 | Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra · PPoPP 2025 |
Data mining
clustering |
0.3 | 1 | 2025 | Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra · PPoPP 2025 |
Data mining › clustering › kernel clustering
kernel k-means |
0.3 | 1 | 2025 | Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra · PPoPP 2025 |
Methods — techniques the papers use, named apart from their topics
sparse linear algebra · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear AlgebraabstractK-means is a popular clustering algorithm with significant applications in numerous scientific and engineering areas. One drawback of K-means is its inability to identify non-linearly separable clusters, which may lead to inaccurate solutions in certain cases. Kernel K-means is a variant of classical K-means that can find non-linearly separable clusters. However, it scales quadratically with respect to the size of the dataset, taking several minutes to cluster even medium-sized datasets on traditional CPU-based machines. Julian Bellavita, Thomas Pasquali, Laura Del Rio Martin, Flavio Vella, Giulia Guidi |
PPoPP | 3 |