Presentation of the Microsoft Research-Inria Joint Centre


The Microsoft Research-Inria Joint Centre was founded by Inria (the French National Research Institute for Computer Science and Applied Mathematics), Microsoft Corporation, and the Microsoft Research Laboratory Cambridge. The Centre's objective is to pursue fundamental, long-term research in Computer Science with a particular emphasis on formal methods and machine learning and some of their key applications.

In April 2005, the French Minister for Research, Gilles Kahn, Chairman of Inria , and Steve Ballmer, Chief Executive Officer of Microsoft Corporation, signed a memorandum of understanding and announced the creation of a joint laboratory in France. In October 2005,  the French Minister for Research, Gilles Kahn, and Bill Gates, founder of Microsoft, signed a framework agreement.  A new laboratory was created on the Plateau de Saclay, near the campus of INRIA Saclay, University of Orsay, Ecole Polytechnique, and Supelec.  Three research projects on Formal Methods and Security were launched in May 2006.

The official inauguration of the Joint Centre took place at Supelec on January 12, 2007, in the presence of Eric Boustouller, Chairman of Microsoft France, Michel Cosnard, Chairman of Inria, Andrew Herbert, Managing Director of Microsoft Research Cambridge, and Rick Rashid, Senior Vice President of Microsoft-Head of Microsoft Research.

The research programme at the Joint Centre is structured in four distinct areas, namely: i) formal methods and their applications, ii) machine learning and big data, iii) computer vision and medical imaging, and iv) social information networks and privacy.

Formal methods and their applications

The Mathematical Components project aims to develop the ability of existing proof assistants like Coq to automatically check difficult proofs in mathematics. After teaching such proof assistants advanced notions of algebra, which culminated in the computer-certified proof of the Feit-Thompson theorem, our researchers plan to augment proof assistants’ abilities to encompass advanced notions of mathematical analysis.

The Tools for Proofs project addresses challenges raised in certifying correct behavior of distributed and concurrent systems, in which there is no certainty as to when distinct components will interact. Specifically our researchers elaborate the TLA+ specification and proof language to make it an operational tool for specifying and certifying properties of distributed systems.

The Secure Computing project develops new languages and associated certification tools to prove that implementations of cryptographic protocols are sound, thereby improving the security of Internet transactions. It also develops frameworks to generate certified Javascript code.

Machine learning and big data

The Large-scale structured machine learning project develops new methods for achieving efficient trade-offs between statistical accuracy and computational cost. It also develops algorithms efficiently trading off exploration with exploitation in active learning scenarios.

The Z-Cloud workflows project develops solutions for efficiently instantiating workflows in a cloud computing environment by mapping tasks of the workflow to specific machines. It conjointly optimizes the replication of data within the cloud computing nodes.

The Interactive network visualization project develops tools for interacting with and visualizing data that arises from both online social networks and brain imagery, with a particular emphasis on time series.

The White box search-based software engineering project uses machine learning to improve software engineering by automatically determining software parameters and assisting developers through recommendation of code snippets.

Computer vision and medical imaging

The Video understanding project aims to automatically extract rich features from large video catalogues to support semantically rich queries when searching such catalogues.

The Medilearn project develops heart models accurately personalized to assist diagnosis and therapy selection for patients with a heart condition. It also focuses on identification of human brain activation patterns induced by conducting specific cognitive tasks.

Social networks and privacy

The social information networks project develops efficient recommendation of contacts and contents to users of online social networks. It also addresses design of reward schemes for incentivizing efficient filtering of information by such users.

The privacy-friendly services and apps project develops means for users to protect their private information such as geo-localization traces while preserving the ability of applications to provide value-added services to such users.

The Joint Centre involves overall 100 researchers with 40 permanent researchers from Inria, 30 permanent researchers from Microsoft Research, and 30 non-permanent researchers (interns, postdoctoral and PhD students).

The Joint Centre benefits from the excellence of research at Inria and Microsoft Research. It demonstrates Inria's and Microsoft Research's shared vision of the importance of computer science research in the sciences and the global economy.

And, crucially, it is a commitment to open, academic research: the ideas, technologies, publications, and software produced by the Joint Centre are all publicly available.