Alsayed Algergawy
Anahita Kazem
Andreas Ostrowski
Birgitta König-Ries
Carola Eichner
Cornelia Fürstenau
David Schöne
Dina Sharafeldeen
Eleonora Petzold
Felicitas Löffler
Frank Löffler
Franziska Zander
Hamdi Hamed
Jan Martin Keil
Jihen Amara
Jitendra Gaikwad
Leila Feddoul
Luiz Gadelha
Martin Hohmuth
Neha Gupta
Nora Abdelmageed
Pawandeep Kaur
Roman Gerlach
Samira Babalou
Sheeba Samuel
Susanne M Hoffmann
Sven Thiel
Trupti Padiya
Vamsi Krishna Kommineni
Dr. Sheeba Samuel
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PostDoc Researcher Ernst-Abbe-Platz 2 , 07743 Jena , Room: 3211
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Curriculum Vitae
Past Positions:
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)
Education:
PhD, 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)
Grants
- ProChance 2017 Grant, Friedrich Schiller University Jena
Promotion of the scientific interaction of young female researchers. - IMPULSE project 2020, Friedrich Schiller University Jena
Support Programme for early and advanced postdocs to apply for own third-party funds. Funding code: IP 2020-10 - Start-up funding from MSCJ for the project “Integrating Knowledge Graphs for DL Interpretability”
Work
- PhD Dissertation: A Provenance-based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments
- PhD Dissertation Defense Slides
- REPRODUCE-ME Ontology
- ProvBook
- CAESAR
- ReproduceMeGit
More Information about my work is available here
Professional Activities
- Invited Speaker Talk on “Contributions to Open Science for Reproducible Research” in QPTData Open Science Workshop at FIZ Karlsruhe, 2020. [Slides]
- Invited Speaker Talk on “The Story of an Open Science Experiment” in Open Science Days at Max Planck Society, Berlin, 2020.
- Reviewer for JupyterCon 2020
- Speaker Talk at JupyterCon 2020
- Organizing Committee of Machine Learning Summer School 2020.
- Co-organizer of the workshop “Fostering reproducible science – What data management tools can do and should do for you“, 2017, Germany.
Research Area
My research area includes:
- Scientific Data Management and processing
- Reproducibility of Scientific Experiments
- Semantic Web
- Data provenance
- Machine Learning
Publications
2020

Sheeba Samuel and Birgitta König-Ries
Provenance Week 2020
Charlotte, North Carolina, USA 22.6.2020

Sheeba Samuel, Frank Löffler and Birgitta König-Ries
Provenance Week 2020
Charlotte, North Carolina, USA 22.6.2020

Sheeba Samuel, Maha Shadaydeh, Sebastian Böcker, Bernd Brügmann, Solveig Franziska Bucher, Volker Deckert, Joachim Denzler, Peter Dittrich, Ferdinand von Eggeling, Daniel Güllmar, Orlando Guntinas-Lichius, Birgitta König-Ries, Frank Löffler, Lutz Maicher, Manja Marz, Mirco Migliavacca, Jürgen R. Reichenbach, Markus Reichstein, Christine Römermann, Andrea Wittig
Research Ideas and Outcomes
11.5.2020
2019

Sheeba Samuel
Friedrich Schiller University Jena 20.12.2019
2018

Sheeba Samuel, Kathrin Groeneveld, Frank Taubert, Daniel Walther, Tom Kache, Teresa Langenstück, Birgitta König-Ries, H. Martin Bücker and Christoph Biskup
11th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2018)
Antwerp, Belgium 5.12.2018

Sheeba Samuel and Birgitta König-Ries
The 17th International Semantic Web Conference (ISWC) Demo Track 2018
Monterey, California, USA 10.10.2018

Sheeba Samuel and Birgitta König-Ries
15th Extended Semantic Web Conference (ESWC) Posters & Demo 2018
Crete, Greece 6.6.2018
2017

Sheeba Samuel
14th Extended Semantic Web Conference (ESWC) 2017
Portoroz, Slovenia 28.5.2017

Sheeba Samuel, Birgitta König-Ries
14th Extended Semantic Web Conference (ESWC) 2017
Portoroz, Slovenia 28.5.2017
2016

Sheeba Samuel, Frank Taubert, Daniel Walther, Birgitta König-Ries and H. Martin Bücker
First International Workshop on Reproducible Open Science, 2016 co-located with TPDL
Hannover, Germany 9.9.2016
Talks
2020

JupyterCon 2020
JupyterCon 2020 13.10.2020

JupyterCon 2020
JupyterCon 2020 13.10.2020

Provenance Week 2020
Virtual Provenance Week 2020 22.6.2020

Virtual Provenance Week
Provenance Week 2020 22.6.2020

QPTData Open Science Workshop
FIZ Karlsruhe 23.1.2020
2019

PhD Dissertation Defense
Friedrich Schiller University Jena 20.12.2019
2018

11th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2018)
Antwerp, Belgium 4.12.2018

ICEI2018 - The 10th International Conference on Ecological Informatics
Jena, Germany 27.9.2018
2017

14th Extended Semantic Web Conference (ESWC) 2017 PhD Symposium
Portoroz, Slovenia 29.5.2017
2016

First International Workshop on Reproducible Open Science (RepScience 2016)
Hannover, Germany 9.9.2016
Teaching
WS 2019/2020
WS 2018/2019
SS 2018
WS 2017/2018
Current Projects
![]() | A virtual “Werkstatt” for digitization in the sciences Data is central in almost all scientific disciplines nowadays. Furthermore, intelligent systems have developed rapidly in recent years, so that in many disciplines the expectation is emerging that with the help of intelligent systems, significant challenges can be overcome and science can be done in completely new ways. In order for this to succeed, however, first, fundamental research in computer science is still required, and, second, generic tools must be developed on which specialized solutions can be built. A virtual manufactory for digitization in the sciences, the “Werkstatt”, which is being established at the Michael Stifel Center Jena (MSCJ) for data-driven and simulation science is a collaborative project funded by the Carl Zeiss Foundation to address fundamental questions in computer science and applications. The Werkstatt focuses on three key areas, which include generic tools for machine learning, knowledge generation using machine learning processes, and semantic methods for the data life cycle, as well as the application of these topics in different disciplines. Core and pilot projects address the key aspects of the topics and form the basis for sustainable work in the Werkstatt. Read More |
Semantic Annotations for Building a Reproducible and Interoperable Solution for End-to-End Machine Learning Pipelines Machine learning (ML) is becoming an increasingly important scientific area supporting decision making and knowledge generation and is creating a high impact on several fields including healthcare, education, and many more. With this, it also becomes more and more important that the results of ML experiments are reproducible. The goal of this proposed project is to enable reproducibility and interoperability of end-to-end ML pipelines via the development and exploitation of semantic annotations. The provenance information along with the semantic annotations will be used to make ML experiments more comparable, thus having a basis to judge progress of the field. The aim of the overall project is the application of semantic technologies to ML pipelines for efficient provenance capture, representation, query, comparison, and visualization to enable reproducibility and interoperability. The project is funded through the IMPULSE program of Friedrich-Schiller University Jena. Read More |
Completed Projects
![]() | Project Z2 of CRC ReceptorLight: Integrative Data Management and Processing Project Z2 of CRC ReceptorLight is jointly carried out by the Jena University Hospital and Friedrich Schiller University Jena within the framework of the Collaborative Research Centre (CRC) "High-end light microscopy elucidates membrane receptor function (Receptor Light)" established in 2015. CRC ReceptorLight is funded by the German Research Foundation (DFG) and consists of around 20 research projects from various institutions located in Jena and Würzburg. It will apply and develop light microscopy techniques with highest resolutions in space and time. The volume of the resulting datasets will be large, expected to be in multi-petabyte data. The overall goal of project… Read More |