Demo Data from EDBT 2026 / reviewers in the wild / expert
Alicia Aarnio
dblp:352/8801
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
topological data analysis |
0.7 | 1 | 2023 | Toplogical Data Analysis Detects and Classifies Sunspots (Student Abstract) · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
topological data analysis · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Toplogical Data Analysis Detects and Classifies Sunspots (Student Abstract)abstractIn our technology-dependent modern world, it is imperative to monitor the Sun for space weather threats to critical infrastructure. Topological data analysis (TDA) is a new set of mathematical techniques used in data analysis and machine learning. We demonstrate that TDA can robustly detect and classify solar surface and coronal activity. This technique is a promising step toward future application in predictive space weather modeling. Aidan Lytle, Neil Pritchard, Alicia Aarnio, Thomas Weighill |
AAAI | 3 |