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Dr Fabian Stephany

Departmental Research Lecturer

Dr Fabian Stephany

Departmental Research Lecturer

About

Fabian Stephany is a Departmental Research Lecturer in AI & Work at the Oxford Internet Institute (OII), University of Oxford, and a Research Affiliate at the Humboldt Institute for Internet and Society in Berlin. With this current project on the future of creative work, Fabian investigates how we can create more sustainable jobs via data-driven reskilling in times of technological disruption. He is a co-creator of the Online Labour Observatory – a digital data hub, hosted by the OII and the International Labour Organisation, for researchers, policy makers, journalists, and the public interested in online platform work. His research has been published in leading academic journals and was covered by Washington Post, The New York Times, The Telegraph, The Statesman, Nikkei Asia, and other popular media around the world.

Fabian holds a PhD and degrees in Economics and Social Sciences from different European institutions, including Universitá Bocconi Milan and University of Cambridge. As an Economist and Senior Data Scientist, Fabian has been working in the private sector and for various actors in the international policy landscape, such as the United Nations Development Programme, the World Bank or the OECD in Paris.

Research Interests

Future of Work, Internet Economics, Network Science, Online Gig Economy, Platform Economy

Positions at the OII

  • Departmental Research Lecturer, March 2022 -
  • Researcher, November 2019 - March 2022

Research

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Projects

News & Press

Teaching

Current Courses

Research Design for Social Data Science

This course introduces students to conceptual and methodological aspects of social science research methods, including both quantitative and qualitative methods.

Computational Methods for the Social Sciences

This course teaches the essentials of programming in Python, using the language to access data from a diverse variety of sources on the social web, and transforming this material into datasets which are amenable to traditional social science analysis.