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 VLDB 2026 / reviewers in the wild / expert

Tytus Grodzicki

dblp:399/2042 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0005-0440-2511ORCID · 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.

Theoretical computer science
1 paper
Distributed computing theory · 93% Algorithms and data structures · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed computing theory
leader election		 0.912025
Improving Efficiency in Near-State and State-Optimal Self-Stabilising Leader Election Population Protocols · PODC 2025
Distributed computing theory
population protocols		 0.912025
Improving Efficiency in Near-State and State-Optimal Self-Stabilising Leader Election Population Protocols · PODC 2025
Distributed computing theory
self-stabilization		 0.912025
Improving Efficiency in Near-State and State-Optimal Self-Stabilising Leader Election Population Protocols · PODC 2025
Distributed computing theory › self-stabilization
self-stabilizing leader election		 0.912025
Improving Efficiency in Near-State and State-Optimal Self-Stabilising Leader Election Population Protocols · PODC 2025
Algorithms and data structures
ranking		 0.312025
Improving Efficiency in Near-State and State-Optimal Self-Stabilising Leader Election Population Protocols · PODC 2025

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

state-space analysis · 0.9
YearPublicationVenuePosition
2025 Improving Efficiency in Near-State and State-Optimal Self-Stabilising Leader Election Population Protocols
abstract
We study leader election problem via ranking within self-stabilising population protocols. In this scenario, the agent's state space comprises n rank states and x extra states. The initial configuration of n agents consists of arbitrary arrangements of rank and extra states, with the objective of self-ranking. Specifically, each agent is tasked with stabilising in a unique rank state silently, implying that after stabilisation, each agent remains in its designated state indefinitely.
Leszek Gasieniec, Tytus Grodzicki, Grzegorz Stachowiak
 PODC2