Luc Rocher is the Director of the DPhil Programme in Social Data Science and is a lecturer at the Oxford Internet Institute, a junior research fellow at Kellogg College, and a fellow of 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 Scientific Data, Usenix Security, JMLR, WWW) and is regularly covered in the press (New York Times, The Guardian, The Telegraph, Forbes, El Pais, Scientific American) as well as featured in BBC World Service, France TV, RTBF TV and Radio, Radio Canada.
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.
Privacy-preserving technologies for social good, regulation of algorithms in online platforms, privacy and anonymity in social media, participatory science, hacktivism; computational social science and social computing (network science, reinforcement learning, natural language processing, bayesian statistics).
I conduct my research in line with the University's academic integrity code of practice.
29 November 2022
Having given expert evidence on the risks of using data proxies for bias monitoring at a workshop hosted by the Centre for Data and Ethics Innovation (CDEI) at the Department for Digital, Culture, Media & Sport. Dr Luc Rocher shares their insights.
DPhil Student
Andrew holds a B.S. in Applied Mathematics from Yale University and an MSc in Social Data Science from the OII. He is a Clarendon Scholar and was previously a Thouron Prize winner at the University of Cambridge (Pembroke College).
DPhil Student
Manuel is a DPhil student in Social Data Science. He is broadly interested in using Natural Language Processing to extract policy-relevant information from social media data.
Former MSc Student
Eglė was a Shirley Scholar at the Oxford Internet Institute. She holds a degree in Political Science with a minor in Economics from New York University Abu Dhabi.
Former MSc Student
Sydney Kessler holds a BS in Cognitive Science from UC San Diego. Formally a research associate in computational neuroscience, Sydney is especially interested in using AI, NLP, and data scraping as a means of studying social media subcultures.
Former MSc Student
Kaley is an MSc in Social Data Science student at the OII. She is passionate about research and education on systems of oppression. Kaley holds a BS in Criminology and previously worked in crime analysis.
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.
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.
This course teaches the essentials of programming in Python, the language of choice in the growing field of computational social science.
