HIPERCOM2 Team
© Inria / Photo H. Raguet
Inria is glad to announce the selection of 4 new Associate Teams with Californian universities (University of California, Berkeley and Stanford University) as part of the 2015 Inria Associated Team call. The four teams created in 2015 for 3 years.
In addition, two teams that were created in 2012 (FASTLA and ORESTE) have been renewed for 3 years.
The Inria@SiliconValley program counts 15 ongoing Associated Teams in 2015. See the Research Teams page for the full list.
4 new Associate Teams selected in 2015:
- GOAL led by Bernd Sturmfels (UC Berkeley) and Jean-Charles Faugere (POLSYS, Inria Paris-Rocquencourt) on “Geometry and Optimization with ALgebraic methods”: GOAL develops algorithms and mathematical tools to solve geometric and optimization problems through algebraic techniques. As a long-term objective, the joint team plans to develop new software to solve these problems more efficiently. This objective encompasses the challenge of identifying instances of these problems that can be solved in polynomial time with respect to the number of solutions and modeling these problems with polynomial equations.
- MetaMRI led by Russ Poldrack (Stanford University) and Bertrand Thirion (PARIETAL, Inria Saclay Ile-de-France) on “Machine learning for meta-analysis of functional neuroimaging data”: Neuroimaging produces huge amounts of complex data that are used to better understand the relations between brain structure and function. Observing that the neuroimaging community is still largely missing appropriate tools to store and organize the knowledge related to the data, Parietal team and Poldrack’s lab, have decided to join forces to set up a framework for functional brain image meta-analysis, i.e. a framework in which several datasets can be jointly analyzed in order to accumulate information on the functional specialization of brain regions. MetaMRI builds upon Poldrack’s lab expertise in handling, sharing and analyzing multi-protocol data and Parietal’s recent developments of machine learning libraries to develop a new generation of meta-analytic tools.
- GeomStats led by Susan Holmes (Stanford University) and Xavier Pennec (ASCLEPIOS; Inria Sophia Antipolis) on “Geometric Statistics in Computational Anatomy: Non-linear Subspace Learning Beyond the Riemannian Structure”: The scientific goal of GeomStats is to develop the field of geometric statistics with key applications in computational anatomy. The research directions have been broken into three axes, the first two being methodologically driven and the last one being application driven. The first axis aims at generalizing the statistical framework from Riemannian to more general geometric structures and even non-manifold spaces (e.g. stratified spaces). The goal is to understand what is gained or lost using each geometric structure. The second axis aims at developing subspace learning methods in non-linear manifolds. This objective contrasts with most manifold learning methods which learn (locally linear but globally non-linear) subspaces embedded in a large enough Euclidean space. The third scientific direction is application driven with cross-sectional and longitudinal brain neuroimaging studies. The goal is to extract reduced models of the brain anatomy that best describe and discriminate the populations under study.
- REALMS led by Prof. Steven Glaser (UC Berkeley), Prof. Branko Kerkez (Uni. Michigan) and Dr. Thomas Watteyne (HIPERCOM2; Inria Paris-Rocquencourt) on “Real-Time Real-World Monitoring Systems”: REALMS conducts research across the environmental engineering and networking research domains. Its 3-year goal is to develop easy-to-use real-world network monitoring solutions to provide real-time data for environmental and urban applications. Issues that are investigated span: Leveraging long-term large-scale public data set, Modeling TSCH (Time Synchronized Channel Hopping) networks, and System architecture for real-world real-time monitoring together with associated open-source ecosystem.
Find out more about Inria@SiliconValley joint research teams
