Machine Learning, Neuroimaging, Scientific computing, Software Engineering, Bioinformatics, High Dimensional Data. Machine Learning in Neuroimaging
- 2008-now: Research Scientist at NeuroSpin, CEA, Paris-Saclay, France.
- 2005-2008: R&D Engineer in the INSERM Research Unit -“Neuroimaging and Psychiatry”, Orsay, France.
- 2003-2004: Postdoctoral position at CEA, Orsay, France.
- 2002 Software Engineer at MBD.A (Matra BAe Dynamics, EADS company) Velizy, contract for ASTEK company, France.
- 2001-2002: Teaching and Research Assistant at Rennes 1 University, France.
- 1999-2001 Ph.D. in Image Processing at LTSI (laboratory of signal and image processing) of Rennes 1 University, France.
- 1997-1998 Master’s degree in Signal/Image Processing. in Rennes 1 University, France.
- 1992-1997 Master’s degree in Software Engineering. École Pour l’Informatique et les Techniques Avancées (EPITA), Kremlin Bicêtre, France.
Bio (IEEE style)
Edouard Duchesnay received the engineer’s degree in software engineering from École Pour l’Informatique et les Techniques Avancées (France) in 1997, the M.Sc. degree in signal/image processing from Rennes 1 University (France) in 1998 and the Ph.D. degree in signal and image processing Rennes 1 University (France) in 2001.
Since 2008, E. Duchesnay is a research scientist at NeuroSpin/CEA: an MRI neuroimaging center within the CEA. He developed multivariate machine learning algorithms (ML) classification/regression to capture complex relationships to make inferences at an individual level in the perspective of computer-aided diagnosis/prognosis or biomarkers discovery for brain diseases (Duchesnay et al., NeuroImage, 2011).
To investigate genetic influence on the brain, he proposed multivariate latent variable models (Le Floch et al, NeuroImage, 2012) that integrate sparsity and specific feature selection within learning algorithms to alleviate large dimensionality of both imaging and genetic data.
Given the limitations of state-of-the-art sparse algorithms to produce stable and interpretable predictive signatures, he proposed to push forward the regularization approaches extending classical algorithms with structural constraints issued from the known biological structure (spatial structure of the brain and the linkage disequilibrium or pathways of OMICs data) in order to force the solution to adhere to biological priors, producing more plausible interpretable solutions.
The result is a ML library in Python dedicated to high dimensional structured input data (Hadj-Selem et al., arXiv:1605.09658 preprint, 2016, in revision in IEEE-TMI, De Pierrefeu et al., IEEE-TMI, 2017) and ParsimonY that is now used to identify neuroimaging signatures of brain disorders (De Pierrefeu et al., Hum. Brain Mapp., 2018).
- Publications: 58(a), 96(b)
- Total Number of Citations: 4,258(a), 10,962(b)
- H-Index: 18(a), 22(b)
(a)Web of Science, (b)Google scholar
Five most significant scientific articles
A. de Pierrefeu, T. Fovet, F. Hadj-Selem, T. Löfstedt, P. Ciuciu, S. Lefebvre, P. Thomas, R. Lopes, R. Jardri, and E. Duchesnay. “Prediction of activation patterns preceding hallucinations in patients with schizophre- nia using machine learning with structured sparsity“. In: Human Brain Mapping (Jan. 2018).
A. de Pierrefeu, T. Lofstedt, F. Hadj-Selem, M. Dubois, R. Jardri, T. Fovet, P. Ciuciu, V. Frouin, and E. Duchesnay. “Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty“. In: IEEE Transactions on Medical Imaging 37.2 (Feb. 2018), pp. 396–407.
E. Jouvent, E. Duchesnay, F. Hadj-Selem, F. De Guio, J.-F. Mangin, D. Hervé, M. Duering, S. Ropele, R. Schmidt, M. Dichgans, et al. “Prediction of 3-year clinical course in CADASIL“. In: Neurology 87.17 (2016), pp. 1787–1795.
F. Hadj-Selem, T. Lofstedt, E. Dohmatob, V. Frouin, M. Dubois, V. Guillemot, and E. Duchesnay. ”Con- tinuation of Nesterov’s Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging“. Major revision in IEEE Transactions on Medical Imaging. Preprint: arXiv:1605.09658 [stat] (May 2016). arXiv: 1605.09658.
E. Le Floch, V. Guillemot, V. Frouin, P. Pinel, C. Lalanne, L. Trinchera, A. Tenenhaus, A. Moreno, M. Zil- bovicius, T. Bourgeron, S. Dehaene, B. Thirion, J.-B. Poline, and E. Duchesnay. “Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares“. In: NeuroImage 63.1 (Oct. 2012), pp. 11–24.
PCT/FR2010/050431: Inventors: Duchesnay, Edouard; Paillere, Marie-Laure; Cachia, Arnaud; Martinot, Jean-Luc; Artiges, Eric. ”Method for Developing an Information Prediction Device, Use Thereof, and Corresponding Storage Medium and Apparatus“.
2015-Now (35h) - Machine learning in Master 2 - Innovation, marché et science des données (IMSD), Paris-Saclay University. Academic head: Maria-Eugenia Sanin.
2016-2017 (10h) - Data analysis in Master 1 - Mathématiques et applications - option : “Ingénierie mathématique pour les sciences du vivant“, Paris Descartes University. Academic head: Etienne Birmele.
2017-Now (15h) - Biostatistics 3rd year of CentralSupelec, Paris-Saclay University. Academic head: Arthur Tenenhaus.
Pauline Favre (Post-doc, 2017-now) together with JF Mangin and J Houenou.
Amicie de Pierrefeu (PhD, 2016-now).
Pietro Gori (Post-doc, 2016) together with JF Mangin and J Houenou. P. Gori is now Assistant Professor at Télécom ParisTech, Paris, France.
Fouad Hadj Selem (Post-doc, 2013-2015). F. Hadj Selem is now research scientist at the Energy Transition Institute: VeDeCoM, France.
Tommy Lofstedt (Post-doc, 2013-2015). T. Lofstedt is now research scientist in Umea University, Sweden.
Mathieu Dubois (Post-doc, 2013-2014). Mathieu Dubois is now research engineer at CEA Genoscope, Evry, France.
Clémence Pinaud (Engineer trainee 2014). C Pinaud is now engineer at Dreem Co. ( https://dreem.com) Paris.
Jinpeng Li (Research Engineer 2013-2014). J. Li is now engineer at Qynapse Co. ( http://www.qynapse.com/) Paris.
Cecilia Damon (PhD defended on 2011) together with JB Poline. C. Damon is now engineer at Qynapse Co. ( http://www.qynapse.com/) Paris.
Edith Lefloch (PhD defended on 2012). E Lefloch is now research scientist at CEA, CNRGH Evry, France.
2018-2023: R-LiNK (H2020-SC1-2017, 754907). Optimizing response to Li treatment through personalized evaluation of individuals with bipolar I disorder: the R-LiNK initiative, PI: F. Bellivier, WP leader: E Duchesnay and leader for the CEA, Team budget: 800k€.
2014-2018: BIP-Li7 (ANR-14-CE15-0003). Therapeutic Lithium response in Bipolar Disorders and brain Lithium-7 NMR Spectroscopy Imaging at 7 Tesla, PI: F. Bellivier, WP leader:F Boumezbeur, Team budget: 280k€.
2011-2015: MESCOG, (FP6 ERA-NET NEURON 01 EW1207) WP co-leader: E Duchesnay, Team budget: 195k€.
2012-2016: BRAINOMICS (ANR-10-BINF-04), WP leader: E Duchesnay, Team budget: 800k€.
2010-2013: Karamétia (ANR-09-BLAN-0332), PI: E Duchesnay, Team budget: 200k€.
2007-2010: AGIR (ANR-07-NEUR-0001)WP leader: E Duchesnay, Team budget: 150k€.
2007-Present: Contribution to the CATI platform which is a national platform created by the French Alzheimer plan in 2011 to support multicenter neuroimaging studies (9M€ grant).