RenSho
Creating a comprehensive platform to enhance reproducibility in data-science driven research with Renku and Shogun
Abstract
The first goal of this project was to provide Renku with a scheme for integrating external ML libraries, to allow broadening of the methodology that is accessible through the platform.
This includes providing schemes on how to connect external ML libraries to Renku’s internal workflow tools, and user templates that empower non-experts to embedding third party methodology into their workflows without detailed technical knowledge as well as defining an ontology to be able to formally process results of machine learning models within Renku.
The second goal of this project was to specifically integrate Shogun within Renku. This provided the significant efforts that went into reproducibility and transparency of the various models/algorithms within Shogun to the Renku community.
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Rok obtained a B.A. in Physics from Washington University in St.Louis in 2003. After obtaining his PhD in theoretical Astrophysics from the University of Washington in 2010, Rok spent several years as a Postdoctoral researcher at the Institute for Computational Science, University of Zürich. Seeking new challenges, he moved to the ETH Scientific IT Services group, where he helped researchers across different ETH domains solve their (big) data analysis problems. He specialized in optimizing and scaling up data analysis tasks by mapping them to high-performance computing resources. Since July 2017 he has been at the Swiss Data Science Center developing Renku, the Center's data science platform.
PI | Partners:
Co-PIs:
- Gunnar Rätsch, ETH Zürich
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