Elamur Research⌕
☰
home / authors / Simone Kopeinik

Simone Kopeinik

Simone Kopeinik has 2 papers in our library, in 2023–2024; often with Dieter Theiler, Dominik Kowald, Harald Semmelrock.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 13 · 84 works · 535 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 10 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8335

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Know Center Research GmbHGraz · Austria2019–2026 · 30 papersEmployment 2019–2022 · Senior Researcher, Social ComputingEmployment since 2023 · Deputy Research Area Manager; Key Researcher, FAIR AIcurrent🎓Graz University of TechnologyGraz · Austria2009–2018 · 19 papersEducation 2012–2017 · Dr. techn., Computer ScienceEmployment 2010–2018 · University Project Assistant, Cognitive Science Section, Institute of Interactive Systems and Data Science🎓FH JOANNEUM University of Applied SciencesGraz · Austria2024 · 1 paperEmployment since 2024 · Lecturer

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksDecision Sciences · 2 worksInformation Systems · 2 worksInformation Systems and Management · 2 worksArtificial Intelligence · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers (2024 · 92 citations)
    The authors argue that ML-based research has a serious reproducibility problem and that the community still underestimates it.
  2. Reproducibility in Machine Learning-Driven Research (2023 · 11 citations)
    This mini survey examines why so many machine learning (ML) results cannot be reproduced, and what might help.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Reproducibility in Machine Learning-based Research: Overview, Barriers and DriversAI MagazineHarald Semmelrock, Tony Ross-Hellauer, Simone Kopeinik, et al.2024 · 92 citations
  2. Reproducibility in Machine Learning-Driven ResearcharXiv:2307.10320Harald Semmelrock, Simone Kopeinik, Dieter Theiler, et al.2023 · 11 citations
  3. Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language ↗Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP) · no summary yetKristin Gnadt et al.2025

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 10 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/simone-kopeinik/