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SwissAnon
Team

The people behind SwissAnon

A network across FHNW, BFH, UZH, and ZHAW — researchers and practitioners working on data anonymization and synthetic data.

Core team

  • Matthias Templ

    Matthias Templ Lead

    Professor for Empirical Methods and Business Analytics

    FHNW · ETH Zürich

    Works on compositional data analysis, imputation of missing values, and data anonymization. Editor-in-Chief of the Austrian Journal of Statistics, author of a textbook on statistical disclosure control, and maintainer of the sdcMicro and simPop R packages — on CRAN since 2010.

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  • Murat Sariyar

    Murat Sariyar Co-lead

    Professor for Medical Informatics

    BFH · University of Bern

    A mathematician whose interests span anonymization, record linkage, deep learning, bioinformatics, pharmacogenomics, natural-language processing, ontologies, and the broader question of identity.

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  • Oscar Thees

    Oscar Thees

    Research associate · PhD student

    FHNW · TU Wien

    An economist researching the anonymization of event and longitudinal data with privacy-preserving technologies.

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  • Roman Müller

    Roman Müller

    PhD student · Research associate

    UZH · FHNW

    Develops anonymization techniques for longitudinal and network data, where each record connects to many others.

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  • Marko Miletic

    Marko Miletic

    Project collaborator & software developer

    BFH — Institute for Optimization and Data Analysis

    Builds anonymization methods for event and longitudinal data, turning research into tools the industry and health sector can run in production.

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Extended team & specialisms

  • Carolin Strobl

    Carolin Strobl

    Head of research unit

    UZH — Department of Psychology

    Leads a research unit in psychological methods and sits on the steering committee of the Center for Reproducible Science. She promotes anonymization and synthetic data as a way to share sensitive data in the spirit of Open Science.

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  • Reto Bürgin

    Reto Bürgin

    Lecturer

    ZHAW — Institute for Data Analysis and Process Design

    Focuses on the practical problems of data anonymization and on the guidelines, illustrations, and help tools that make it usable in day-to-day work.

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