Sheeba Samuel

Postdoctoral Researcher

TU Chemnitz, Germany

Ph.D., Computer Science, Friedrich Schiller University Jena, Germany (2016-2019)

Master of Technology (M Tech), Information Technology, International Institute of Information Technology, Bangalore, India (2011-2013)

Bachelor of Technology (B Tech), Computer Science and Engineering, Cochin University of Science and Technology (CUSAT), India (2007-2011)

Member of Technical Staff II - Aruba, a Hewlett Packard Enterprise Company (July 2013-December 2015)

Graduate Technical Intern - Aruba, a Hewlett Packard Enterprise Company (January 2013-June 2013)

Reproducible research, Data provenance, Scientific data management and processing, Semantic web, Machine learning

Reproducibility Co-Chair in BTW 2023.

PC member of Sustainable Data Analytics Workshop associated with INFORMATIK, (cancelled) 2021.

Organizing Committee of Werkstatt Machine Learning Summer School, 2020.

Local Organizing Committee of the 10th International Conference on Ecological Informatics 2018.

Co-organizer of the workshop “Fostering reproducible science – What data management tools can do and should do for you“, 2017.

PLOS ONE, 2024

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Open Research Europe, 2024

F1000Research, 2024

Expert Systems with Applications, 2023

DE4DS Workshop co-located with BTW, 2023

Earth Science Informatics, 2022

GigaScience Journal, 2021

Frontiers Journal, 2021

Datenbank-Spektrum Journal, 2021

JupyterCon, 2020

Arbeitskreis Data Engineering for Data Science, 2020-present

Michael Stiefel Centre Jena (MSCJ), 2021-present

See full list of talks here

Exploring Computational Reproducibility in Jupyter Notebooks: Insights and Challenges The Spring Symposium 2024 of the Fachgruppe Database Systems (FG DB), March 12, 2024 (Slides, Link)

Reproducible Research with Data Provenance Guest Lecture, University of Passau, 24th January 2024

Reproducible Research: Responding to 6W and 1H Questions of Data Provenance The HEIBRiDS Lecture Series at Einstein Center Digital Future, Berlin, Germany, 5th January 2022 (Slides, Link)

The Story of an Open Science Experiment Max Planck Digital Library (MPDL) Open Science Days 2021, October 20, 2021 (Slides, Link)

Provenance and Reproducibility: a look into Jupyter Notebooks Thuringian RDM Days “Data documentation: A love note to the future!”, June 22, 2021 (Slides, Video, Link)

Contributions to Open Science for Reproducible Research QPTData Open Science Workshop 2020, January 23, 2020 (Slides, Link)

Freistaats Thüringen funding for the research project ‘Explainability and Reproducibility for AI’ (2021-2024)

Start-up funding from MSCJ for the project “Integrating Knowledge Graphs for DL Interpretability” (2020-2021)

IMPULSE project, Friedrich Schiller University Jena, Support Programme for early and advanced postdocs to apply for own third-party funds. Funding code: IP 2020-10 (2020-2021)

ProChance Grant, Friedrich Schiller University Jena, Promotion of the scientific interaction of young female researchers (2017).

SoSe 2024 - Web Engineering Seminar

SoSe 2024 - Pro-/Haupt- und Forschungsseminar VSR

WiSe 2023/ 2024 - Semantic Technologies for Science

SoSe 2021 - Management of Scientific Data

WiSe 2020/ 2021 - Semantic Technologies for Science

SoSe 2020 - Management of Scientific Data

WiSe 2019/ 2020 - Semantic Web Technologies

SoSe 2019 - Management of Scientific Data

SoSe 2019 - Softwareentwicklungsprojekt (SWEP): Project Supervision

WiSe 2018/ 2019 - Semantic Web Technologies

SoSe 2018 - Management of Scientific Data

WiSe 2017/ 18 - Semantic Technologies for Science

PhD Thesis

Waqas Ahmed: Reproducibility for AI, 2021-

Jihen Amara: Integrating Knowledge Graphs for DL Interpretability, 2021-

Master Thesis

Ashok Tanubuddi: A recommendation tool to implement and validate the reproducibility of studies, 2021-2022

Balaramakrishna Paritala: Provenance Tracking in Machine Learning Python Jupyter Notebooks, 2021-2022

Sravan Kumar Devireddy: Reproducibility of Jupyter Notebooks from publications, 2021-2022

Bachelor Thesis

Dominik Kerzel: Provenance-Tracking und -Visualisierung von Maschinellen-Lern-Skripten in Jupyter Notebooks, 2021

Tarek Al Mustafa: Reproducibility of Machine Learning Experiments given the provenance data, 2021

Project

Provenance Management of Datasets and Scripts in BEXIS2, SoSe 2019

See full list here

Integrating domain knowledge for enhanced concept model explainability in Plant Disease classification
Jihen Amara, Sheeba Samuel, Birgitta König-Ries, The 21st European Semantic Web Conference, ESWC In-Use Track, 2024 (To appear)

Computational reproducibility of Jupyter notebooks from biomedical publications
Sheeba Samuel, Daniel Mietchen, 2024 (Link)

From human experts to machines: An LLM supported approach to ontology and knowledge graph construction
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel, 2024 (Preprint)

End-to-End Provenance Representation for the Understandability and Reproducibility of Scientific Experiments using a Semantic Approach
Sheeba Samuel, Birgitta König-Ries, Journal of Biomedical Semantics, 2022 (Paper, Bibtex)

A collaborative semantic-based provenance management platform for reproducibility.
Sheeba Samuel, Birgitta König-Ries, PeerJ Computer Science, 2022 (Paper, Bibtex)

Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles.
Sheeba Samuel, Frank Löffler, Birgitta König-Ries, ProvenanceWeek 2020 (Paper, Bibtex)

Understanding experiments and research practices for reproducibility: an exploratory study
Sheeba Samuel, Birgitta König-Ries, PeerJ 9:e11140, 2021(Paper, Bibtex)

ProvBook: Provenance-based Semantic Enrichment of Interactive Notebooks for Reproducibility
Sheeba Samuel, Birgitta König-Ries, The 17th International Semantic Web Conference (ISWC) 2018 Demo Track, 8-12 October, 2018, Monterey, California, USA (Demo, Paper, Bibtex)