Interaction Data Lab
Research team at CRI 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.
We are glad to announce that our Master’s student intern Stephanie Chuah Shin Ju from last summer was awarded the ‘Excellent Thesis Award’ by the Tsinghua University’s School of Public Policy and Management for her thesis “The Online Transition of the Geneva-Tsinghua Initiative SDG Summer School“. She is also in the running for the university-wideContinue reading “Excellent Thesis Award”
The article is in French! It features our work on the design of recommender systems for collective intelligence at JOGL, and on the evolution of scientific fields. See more in our project page!
This is our first virtual steps as a team website! Please have a look around to meet the team, see our projects, and enjoy some network art in the virtual gallery!
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.