An interdisciplinary research Institute on how data change science and society.
The Grenoble Alpes Data Institute aims to undertake groundbreaking interdisciplinary research focusing on how data change science and society. It combines three fields of data-related research in a unique way: data science applied to spatial and environmental sciences, biology, and health sciences; data-driven research as a major tool in Social Sciences and Humanities; and studies about data governance, security and the protection of data and privacy.

Data Science for Earth, Space and Environmental Sciences

This action (WP1) exploits the huge potential of applying modern data analytics in ESES. It overcomes disciplinary and technical barriers and provide new statistical and computational approaches, in particular for data in astrophysics to better characterize exoplanets, in oceanography to better infer vertical exchange in the ocean, and in ecology to reconstruct interaction networks between species.

Carlos Alberto Gomez Gonzalez

Carlos Gomez is a data scientist. In October 2017, he was recruited by the Data Institute of the University Grenoble Alpes as a Junior Researcher in Data Science for Earth, Space and Environmental Sciences



Raphael Bacher

Raphael Bacher is a research engineer in signal processing and data science. In December 2017, he was recruited by the Data Institute of the University Grenoble Alpes as a Research Engineer to develop collaborative tools for data science teachers and researchers (WP1 & 2).




Data Science for Life Sciences

Life and health sciences are changing rapidly: new technologies provide large-scale individual data (“omics” data) that can be related to health-related outcomes and medical imaging data. Identifying predisposition factors for disease and making predictions about health is a tremendous challenge for data scientists that we address by conceiving original data science methods, algorithms, and software to relate omics data to health-related traits.

Magali Richard

Magali Richard
is a computational biologist specialized in experimental and theoritical genetic. In January 2018, she was recruited by the Data Institute of the University Grenoble Alpes as a Junior Researcher in Data Science for Life Sciences.



Massive and Rich Data for Humanities

This action (WP3) aspires to re-dimension research in the humanities fields, from small isolated corpora of rich data to a large interconnected corpus of rich data. It covers scientific problems ranging from the massive production of rich data, to operating, querying and visualizing voluminous data, through perennial preservation of the data and metadata, thus questioning methodology in humanity research.

Data Science, Social Media and Social Sciences

New data sources coming from the web and social media are made available to analyze social structures in innovative ways. Social media have recently become a promising observatory of society. Social scientists and computer scientists delivers new machine learning methodologies to provide a better understanding of the dynamics of opinions, careers and urban structures.

Sophie Kuegler

In September 2017, Sophie Kuegler was recruited as an engineer for the processing and analysis of databases collected from the web and social networks


Data Governance, Data Protection and Privacy

This action (WP5) aims to analyze, in a multi-disciplinary perspective, why and how specific forms of data governance emerge as well as the consequences on the interaction between the state, the market and society. The focus is on the challenges raised by the collection and use of data for privacy, on the data subjects’ rights and on the obligations of data controllers and processors. A Privacy Impact/Risk assessments methodology and software are proposed. A case study is focus on medical and health data and makes recommendations on how they should be collected and processed.

Raouf Kerkouche

Raouf Kerkouche has been recruited since January 2018 as a PhD student in the Privatics team, at Inria. He's working on "Privacy-Preserving Processing of Medical Data", directed by Claude Castelluccia. The main goal of his project is to study the problem of medical data privacy and propose privacy-preserving solutions. Another objective is to design Privacy-preserving Machine-learning algorithms.