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

Ralf Grubenmann
Ralf Grubenmann
Sr. Software Engineer
Chiara Preti
Chiara Preti
Data Scientist
Ouma Turry
Ouma Turry
PhD Student
Alessandro Mari
Alessandro Mari
Data Scientist
Luis Salamanca
Luis Salamanca
Lead Data Scientist
Carlos Vivar Ríos
Carlos Vivar Ríos
ORD Engineer

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

Efficient and scalable graph generation through iterative local expansion
March 20, 2024

Efficient and scalable graph generation through iterative local expansion

Efficient and scalable graph generation through iterative local expansion

Have you ever considered the complexity of generating large-scale, intricate graphs akin to those that represent the vast relational structures of our world? Our research introduces a pioneering approach to graph generation that tackles the scalability and complexity of creating such expansive, real-world graphs.
RAvaFcast | Automating regional avalanche danger prediction in Switzerland
March 6, 2024

RAvaFcast | Automating regional avalanche danger prediction in Switzerland

RAvaFcast | Automating regional avalanche danger prediction in Switzerland

RAvaFcast is a data-driven model pipeline developed for automated regional avalanche danger forecasting in Switzerland. It combines a recently proposed classifier for avalanche danger prediction at weather stations with a spatial interpolation model and a novel aggregation strategy to estimate the danger levels in predefined wider warning regions, ultimately assembled as an avalanche bulletin.
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.

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