member

Previous page Stanley DURRLEMAN PhD, DR2, INRIA Team “Algorithms, Models and Methods for Images and Signals of the Human Brain ” https://who.rocq.inria.fr/Stanley.Durrleman/ https://twitter.com/SDurrleman https://www.linkedin.com/in/stanley-durrleman-8854722/

Biography

Education - Habilitation to direct research (HDR), Pierre and Marie Curie University (2018) - PhD in applied mathematics, Nice-Sophia Antipolis University (2010) - very honorable mention - Graduate of the Ecole Supérieure des Télécommunications, Paris, (2006) - Master's degree in Mathematics, Vision, Learning (MVA), Ecole Normale Supérieure de Paris Saclay (2005) - Graduate of Ecole Polytechnique (2004) Professional experiences - Research chair at the interdisciplinary institute in artificial intelligence PRAIRIE (2019 -) - Co-leader of the ARAMIS team at the Brain Institute (ICM) (2019 -) - Director and founder of the Neuroinformatics Center at the Paris Brain Institute (2017 - 2021) - INRIA researcher at the Brain Institute, on secondment from the Corps des Mines (2011-) - Post-doctoral fellow at the Scientific Computing and Imaging Institute, University of Utah, USA (2010 - 2011) - PhD student in the Inria Asclepios team under the supervision of Nicholas Ayache (2007-2010)

Research work

Modeling and predicting the progression of neurodegenerative diseases - Integration of multimodal data (imaging, clinical) in computational models - Prediction of the progression of a neurodegenerative disease - Design of decision support tools for patient follow-up or inclusion in clinical trials

Publications

  • AD Course Map charts Alzheimer’s disease progression, I Koval, A Bône, M Louis, …, S Durrleman, Scientific Reports, Vol 11 (1), p 1-16, 2021
  • Learning the spatiotemporal variability in longitudinal shape data sets, A Bône, O Colliot, S Durrleman, International Journal of Computer Vision, Vol 128(12), p 2873-2896, 2020
  • Spatiotemporal propagation of the cortical atrophy during the course of Alzheimer’s Disease: Population and individual patterns, I. Koval, J.-B. Schiratti, A. Routier, M. Bacci, O. Colliot, S. Allassonnière, S. Durrleman, Frontiers in Neurology, Vol 9, p 235, 2018
  • A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations, J.-B. Schiratti, S. Allassonnière, O. Colliot, S. Durrleman, Journal of Machine Learning Research, 18(133):1−33, 2017
  • Morphometry of anatomical shape complexes with dense deformations and sparse parameters, S. Durrleman, M. Prastawa, N. Charon, J. R. Korenberg, S. Joshi, G. Gerig, A. Trouvé, NeuroImage, 101(1):35-49, 2014