Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Shukry Zablah

dblp:324/1472 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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.

Theoretical computer science
1 paper
Algorithms and data structures · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing
parallel algorithms		 0.612022
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract) · AAAI 2022
Algorithms and data structures
parallel algorithms		 0.612022
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract) · AAAI 2022
Algorithms and data structures › randomized algorithms
sampling		 0.612022
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract) · AAAI 2022

Methods — techniques the papers use, named apart from their topics

stratified sampling · 1.1cube method · 1.1
YearPublicationVenuePosition
2022 A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)
abstract
We present a novel parallel algorithm for drawing balanced samples from large populations. When auxiliary variables about the population units are known, balanced sampling improves the quality of the estimations obtained from the sample. Available algorithms, e.g., the cube method, are inherently sequential, and do not scale to large populations. Our parallel algorithm is based on a variant of the cube method for stratified populations. It has the same sample quality as sequential algorithms, and almost ideal parallel speedup.
Alexander W. Lee, Stefan Walzer-Goldfeld, Shukry Zablah, Matteo Riondato
 AAAI3