Dr. Michaël Aupetit works at QCRI since 2014. He is with the Social Computing group.
Before joining QCRI, Michaël worked for 10 years as a research scientist and senior expert in data mining and visual analytics at CEA LIST in Paris Saclay, where he designed decision support systems to solve complex industrial problems in health and security domains.
Michaël initiated and co-organized 5 international workshops. He has been PC member of IEEE VAST, PacificVis, ESANN, and ICANN conferences, and has reviewed hundreds of papers for top journals and conferences, has more than 70 publications, and holds 2 WO and 1 EP patents. He obtained the Habilitation for Research Supervision (HDR) in Computer Science from Paris 11 Orsay University in 2012, and the Ph. D degree in Industrial Engineering from Grenoble National Polytechnic Institute (INPG) in 2001.
Dr. Michaël's research focuses on the use and usability of machine learning, artificial intelligence, topological inference, and information visualization techniques together under the Visual Analytics umbrella, to bridge the gap between machines and humans for exploratory data analysis. He uses his expertise to address top priority challenges of Qatar in eHealth and cyber-security.
Qatar Computing Research Institute; Hamad Bin Khalifa University2019 - Present
Qatar Computing Research Institute; Hamad Bin Khalifa University2014 - 2018
Laboratory for Integration of Systems and Technology; CEA Tech2008 - 2014
Department of Earth and Environmental Science; CEA DAM2004 - 2008
Department of Earth and Environmental Science; CEA DAM2002 - 2004
Paris 11 Orsay University; Orsay/France2012
Grenoble National Polytechnic Institute (INPG); Grenoble/France2001
Montpelier University, Montpellier, France1998
Ecole pour les Etudes et la Recherche en Informatique et Electronique (EERIE); Nîmes/France1998
Linking Techniques with Distortions, Tasks, and Layout Enrichment; IEEE Transactions on Visualization and Computer Graphics 2018; https://doi.org/10.1109/TVCG.2018.28467352018
KinVis: A visualization tool to detect cryptic relatedness in genetic datasets; Bioinformatics 2018; https://doi.org/10.1093/bioinformatics/bty10282018
Computer Graphics Forum 34(3):201-210, 2015; https://doi.org/10.1111/cgf.126322015
a supervised multidimensional scaling technique which preserves the topology of the classes; International Journal of Pattern Recognition and Artificial Intelligence 29(6), 2015; https://doi.org/10.1142/S02180014155100882015
Sanity check for class-coloring-based evaluation of dimension reduction techniques; BELIV 2014:134-141; https://doi.org/10.1145/2669557.26695782014
Sanity check and topological clues for linear and nonlinear mappings; Computer Graphics Forum 30(1):113–125, 2011; https://doi.org/10.1111/j.1467-8659.2010.01835.x2011
Learning topology of a labeled data set with the supervised generative Gaussian graph; Neurocomputing 71(7-9):1283-1299, 2008; https://doi.org/10.1016/j.neucom.2007.12.0282008
Concerning the differentiability of the energy function in vector quantization algorithms; Neural Networks 20:621-630, 2007; https://doi.org/10.1016/j.neunet.2006.11.0062007
Learning Topology with the Generative Gaussian Graph and the EM algorithm; Advances in Neural Information Processing and Systems (NIPS) 18, 2006; https://papers.nips.cc/paper/2922-learning-topology-with-the-generative-...2006
How to help seismic analysts to verify the French seismic bulletin? ; Engineering Applications of Artificial Intelligence 19(7):797-806, 2006; https://doi.org/10.1016/j.engappai.2006.05.0082006
High-dimensional labeled data analysis with topology representing graphs; Neurocomputing 63:139-169, 2005; https://doi.org/10.1016/j.neucom.2004.04.0092005
gamma-Observable Neighbours for Vector Quantization; Neural Networks 15(8-9):1017-1027, 2002; https://doi.org/10.1016/S0893-6080(02)00076-X2002