Interaction Data Lab
Research team at LPI Paris
We use network science and data analysis to decipher collective phenomena at biological and social scales. In particular, we study collaborative learning and solving using network approaches on large empirical datasets, with the end goal to develop tools fostering collective intelligence for social impact.
We leverage the power of large datasets to tackle interdisciplinary projects from various fields: social network analyses of scientific teams, dynamics of Twitter communities, collaborative learning using phone call records, dynamics of contribution and collaborations in open-source communities on GitHub, evolution of scientific fields and trajectories of scientists across knowledge domains, design of recommender systems for open science projects, or perturbation spread in technological and biological networks.
Since December 2018, we are organizing with Liubov Tupikina a network seminar series at LPI Paris. Talks showcase the use of network science in a wide range of disciplines, from physics to mathematics, to archaeology and biology, to social sciences or neurosciences, and are intended for a broad, interdisciplinary audience.
Join our mailing list to get updates!
Journal clubs on open communities and recommender systems
These talks have been organized with the Just One Giant Lab (JOGL) open science community to improve our understanding of crowd science and how to enhance collective intelligence using recommender systems on online platforms.