VLDB 2026 Research / reviewers in the wild / expert
Julian Burella Pérez
dblp:262/3897
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Graph learning · 77% Deep learning architectures and training · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning
topological data analysis |
0.5 | 1 | 2021 | giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration · J. Mach. Learn. Res. 2021 |
Methods — techniques the papers use, named apart from their topics
scikit-learn API · 0.5persistent homology · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data ExplorationabstractWe introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda. Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess |
J. Mach. Learn. Res. | 4 |