Stanley DURRLEMAN
Title: PhD
Function: Team manager, PI
Affiliated entities INRIA
Personal X account
Biography
Training
- Habilitation to direct research (HDR), Université Pierre and Marie Curie (2018)
- Doctorate in applied mathematics, Université Nice-Sophia Antipolis (2010) - summa cum laude
- Graduate of the Ecole Supérieure des Télécommunications, Paris (2006)
- Master's degree in Mathematics, Vision and Learning (MVA), Ecole Normale Supérieure de Paris Saclay (2005)
- Graduate of Ecole Polytechnique (2004)
Professional experience
- Holder of a research chair at the PRAIRIE interdisciplinary institute for artificial intelligence (2019 -)
- Co-leader of the ARAMIS team at the Brain Institute (ICM) (2019 -)
- Director and founder of the Neuroinformatics Centre at the Brain Institute (2017 - 2021)
- INRIA researcher at the Brain Institute, on secondment from the Corps des Mines (2011-)
- Post-doctorate at the Scientific Computing and Imaging Institute, University of Utah, USA (2010 - 2011)
- Doctoral student in the Inria Asclepios team under the supervision of Nicholas Ayache (2007-2010)
Research work
Modelling and predicting the progression of neurodegenerative diseases - Integrating multimodal data (imaging, clinical) into computational models - Predicting the progression of a neurodegenerative disease - Designing decision support tools for patient monitoring 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
Team(s)
Team