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 University
2019 - PresentQatar Computing Research Institute; Hamad Bin Khalifa University
2014 - 2018Laboratory for Integration of Systems and Technology; CEA Tech
2008 - 2014Department of Earth and Environmental Science; CEA DAM
2004 - 2008Department of Earth and Environmental Science; CEA DAM
2002 - 2004Paris 11 Orsay University; Orsay/France
2012Grenoble National Polytechnic Institute (INPG); Grenoble/France
2001Montpelier University, Montpellier, France
1998Ecole pour les Etudes et la Recherche en Informatique et Electronique (EERIE); Nîmes/France
1998Linking Techniques with Distortions, Tasks, and Layout Enrichment; IEEE Transactions on Visualization and Computer Graphics 2018; https://doi.org/10.1109/TVCG.2018.2846735
2018KinVis: A visualization tool to detect cryptic relatedness in genetic datasets; Bioinformatics 2018; https://doi.org/10.1093/bioinformatics/bty1028
2018Computer Graphics Forum 34(3):201-210, 2015; https://doi.org/10.1111/cgf.12632
2015a 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/S0218001415510088
2015Sanity check for class-coloring-based evaluation of dimension reduction techniques; BELIV 2014:134-141; https://doi.org/10.1145/2669557.2669578
2014Sanity 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.x
2011Learning 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.2007.12.028
2008Concerning the differentiability of the energy function in vector quantization algorithms; Neural Networks 20:621-630, 2007; https://doi.org/10.1016/j.neunet.2006.11.006
2007Learning 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-...
2006How 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.2006.05.008
2006High-dimensional labeled data analysis with topology representing graphs; Neurocomputing 63:139-169, 2005; https://doi.org/10.1016/j.neucom.2004.04.009
2005gamma-Observable Neighbours for Vector Quantization; Neural Networks 15(8-9):1017-1027, 2002; https://doi.org/10.1016/S0893-6080(02)00076-X
2002