The Swiss Data Science Center

Enabling data-driven science and innovation for societal impact, and supporting scientists, businesses and organizations in data science.
In 2017, a national Data Science initiative from the ETH Board resulted in the creation of a unique joint venture: the Swiss Data Science Center, bringing together the ETH Zürich, the EPFL, and the PSI . The Center’s mission is to accelerate the use of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, the public institutions and the industrial sector.

A team of senior data scientists and experts in domains such as personalized health & medicine, earth & environmental science, social sciences, digital humanities and economics collaborate on academic and industrial projects. This unique positioning, at the crossroad of academic excellence and a fast-paced business environment, is key to simplifying a complex data science journey.

Team

A diversified team of researchers and professionals in Data Science

Tasko Olevski
Tasko Olevski
Sr. Software Engineer
Shirin Goshtasbpour
Shirin Goshtasbpour
PhD Student
Imen Choura
Imen Choura
Head of Finance
Xiaoran Chen
Xiaoran Chen
Sr. Data Scientist
Sofiane Sarni
Sofiane Sarni
Program Manager
Luis Barba Flores
Luis Barba Flores
Sr. Data Scientist

Open Research

Promoting Open Research

promote open research in data science
At the SDSC, we promote open research practices throughout the entire data lifecycle.

By leveraging our expertise in semantic enablement, data governance, privacy and security, and other key areas, we help our partners and ecosystem actors to adopt open research practices that maximize the impact and reuse of their research outputs.

News

Latest news

PassGPT | Using language models to enhance password security
February 6, 2024

PassGPT | Using language models to enhance password security

PassGPT | Using language models to enhance password security

PassGPT is a Large Language Model for password generation trained on leaked passwords, which can outperform existing methods based on generative adversarial networks by guessing twice as many unseen passwords.
ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning
January 24, 2024

ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning

ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning

Applying machine learning to ecotoxicology could help reduce the number of animal tests, costs, and animals sacrificed while preserving the accuracy of the in vivo tests.
License Flowers | Art and AI at SDSC
February 21, 2024

License Flowers | Art and AI at SDSC

License Flowers | Art and AI at SDSC

An adventure to create art using AI to raise awareness on code licenses

Contact us

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