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Dr Luc Rocher

DPhil Programme Director (SDS), Lecturer

Dr Luc Rocher

DPhil Programme Director (SDS), Lecturer

About

Luc Rocher is the Director of the DPhil Programme in Social Data Science and is a lecturer at the Oxford Internet Institute. Luc is a fellow at Kellogg College and at Imperial College London’s Data Science Institute.

Their research investigates the harms posed by large-scale collections of digital human traces—from social media traces to biometrics—and deployed artificial intelligence technologies, identifying gaps in how technology is regulated and how risks are documented, and proposing better models for academic research using sensitive human data.

Luc specialises in computational modelling approaches to study emerging concerns in algorithmic societies, such as the future of privacy and digital rights as well as the governance of algorithms in digital platforms. Their research develops statistical models to make sense of these complex systems, adversarial machine learning approaches to highlight weaknesses of deployed technologies, and interactive tools for everyone to better understand what makes them more vulnerable to privacy harms online.

Luc’s research provides technical guidance to the challenges AI poses for competition law in digital platforms and data protection regulation online. Their work in Nature Communications for instance demonstrated the limits of traditional techniques to de-identify and widely share ‘anonymous’ data online, calling for better privacy-preserving frameworks to disseminate and analyse personal data online.

Prior to joining Oxford, Luc received a PhD from the Université catholique de Louvain in 2019 and worked as a researcher at the Data Science Institute and Computational Privacy Group of Imperial College London, at the ENS de Lyon, and at the MIT Media Lab.

Their work has been published in peer-reviewed journals and conferences (Nature Communications, Nature Machine Intelligence, Nature Scientific DataUsenix SecurityJMLRWWW) and has been covered by 160+ newspapers (New York TimesThe GuardianThe TelegraphForbesEl PaisScientific American) as well as featured in John Oliver’s Last Week Tonight, BBC World Service, France TV, and Radio Canada. Their research on the limitation of anonymisation practices has been referenced by the European Commission, OECD, World Bank, WEF, FTC, by European data protection authorities, in US legal cases, and led to changes to the UK’s Data Protection Bill.

Luc leads the Observatory of Anonymity, an international interactive website in 89 countries where visitors can find out what makes them more vulnerable to re-identification and where researchers can test the anonymity of their research data.

Pronouns: they/them.

Areas of interest for Doctoral Supervision:

I am looking for motivated students interested in studying the impact of privacy-enhancing technologies and auditing algorithms used by public institutions and online platforms. Interest in mathematical modelling and complex systems appreciated (including bayesian statistics, optimisation, network science, machine learning, Python/Julia programming).

Positions at the OII

  • DPhil Programme Director (SDS), January 2023 -
  • Lecturer, October 2021 -

Research

Integrity Statement

In the past five years, my work has been financially supported by UK and Belgian taxpayers, by the Engineering and Physical Sciences Research Council (EPSRC), Innovate UK, the Information Commissioner’s Office (ICO), the John Fell Fund from Oxford University Press, and the Belgian National Fund for Scientific Research (F.R.S.-FNRS).

I conduct my research in line with the University's academic integrity code of practice.

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Current Courses

Fairness, Accountability, and Transparency in Machine Learning

Integrating historical and cultural context with contemporary scholarship, this course equips students with the technical and conceptual tools to engage critically with machine learning research and practice.

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.

Accessing Research Data from the Social Web

This course teaches the essentials of programming in Python, the language of choice in the growing field of computational social science.