Speakers and hands-on teachers

Ismail Ben Ayed, École de technologie supérieure, Canada

Dr. Ben Ayed received the PhD degree (with the highest honor) in computer vision from the INRS-EMT, Montreal in 2007. He is currently an Associate Professor at the ETS, University of Quebec. Before joining the ETS, he worked for 8 years as a research scientist at GE Healthcare, London, ON. He also holds an adjunct professor appointment at Western University (since 2012). Ismail's research interests include computer vision, optimization, machine learning and their potential applications in medical image analysis.  He co-authored a book, over eighty peer-reviewed publications, mostly published in the top venues in these subject areas, and six US patents.  



Olivier Bernard, CREATIS laboratory, Lyon, France

Dr. Olivier Bernard has an MSc in Electrical Engineering and received a PhD in Medical Image Processing from the University of Lyon (INSA) - France - in 2006. In 2007, he was a postdoctoral research fellow at the Federal Polytechnic Institute of Lausanne (Switzerland) in the laboratory headed by Prof. Michael Unser

In 2007, he became Associate Professor at the French University of Lyon and a member of the CREATIS laboratory (CNRS 5220, INSERM U1044, INSA-Lyon, University of Lyon). In 2008, he obtained the special mention (2nd prize) for best Ph.D. in France awarded by the IEEE Engineering in Medicine and Biology Society. In September 2013, he was an invited professor at Federal Polytechnic Institute of Lausanne (Switzerland) in the laboratory headed by Prof. Jean Philippe Thiran. He was an Associate Editor for the IEEE Transactions on Image Processing Journal (2013-2016) and was a member of the technical committee of the IEEE International Conference on Image Processing and the IEEE International Symposium on Biomedical Imaging (2014).

His current research interests include medical image analysis with a particular attention to cardiac imaging. He has a strong interest in machine learning, image segmentation, motion analysis, statistical modeling, sampling theories and image reconstruction


Christian Desrosiers, École de Technologie Supérieure, Canada

Prof. Desrosiers obtained a Ph.D. in Applied Mathematics from Polytechnique Montreal in 2008, and was a postdoctoral researcher at the University of Minnesota with prof George Karypis. In 2009, he joined École de technologie supérieure (ÉTS) as professor in the Departement of Software and IT Engineering. He is codirector of the Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA) and a member of the REPARTI research network. He has over 100 publications in the fields of machine learning, image processing, computer vision and medical imaging, and has served on the scientific committee of several important conferences in these fields.


Jose Dolz, École de Technologie Supérieure, Canada

Jose Dolz is an Assistant Professor in the Department of Software and IT Engineering at the ETS Montreal. Prior to be appointed Assistant Professor, he was a post-doctoral fellow at the ETS Montreal. Dr. Dolz obtained his B.Sc and M.Sc in the Polytechnic University of Valencia, Spain, and his Ph.D. at the University of Lille 2, France, in 2016. Dr. Dolz was recipient of a Marie-Curie FP7 Fellowship (2013-2016) to pursue his doctoral studies. His current research focuses on deep learning, medical imaging, optimization and learning strategies with limited supervision. He authored over 30 fully peer-reviewed papers, many of which published in the top venues in medical imaging (MICCAI/NeuroImage/MedIA/TMI) and vision (CVPR).


Thomas Grenier, CREATIS laboratory, Lyon, France

Dr. Thomas Grenier is Associate Professor at INSA Lyon Electrical Engineering department and at the CREATIS lab in Lyon, France. His research focuses on longitudinal analysis of medical data to study evolution as Multiple Sclerosis lesions, functional activity (muscle and hydrocephaly). Most of these studies involve a segmentation task and dedicated pre and post processing steps. Clustering (spatio-temporal mean-shift), semi-supervised (multi-atlas with machine learning) or fully supervised (DNN) schemes are used to solve such problems considering their specific constraints.


Mohammad Havaei, lmagia inc.

Dr.Havaei is a research scientist at imagia. He focuses on developing machine learning methods applicable to healthcare. His research interests include representation learning, meta-learning and uncertainty. From 2016 to 2018 he was a member of Montreal Institute for Learning Algorithms (MILA) as a postdoctoral fellow under the supervision of Aaron Courville. Prior to that, he obtained my PhD at the University of Sherbrooke under the supervision of Hugo Larochelle and Pierre-Marc Jodoin. The main focus of his PhD was developing deep learning models applied to medical images for brain tumor segmentation.


Pierre-Marc Jodoin, University de Sherbrooke, Canada

Pierre-Marc Jodoin is from  the University of Sherbrooke, Canada where he works as a full professor since 2007.  He specializes in the development of novel techniques for machine learning and deep learning applied to computer vision and medical imaging.   He mostly works in video analytics and brain and cardiac image analytics.  He is the co-director of the Sherbrooke AI plateform and co-founder of the medical imaging company called "Imeka.ca" which specializes in MRI brain image analytics. web site: http://info.usherbrooke.ca/pmjodoin/

Herve Lombaert École de Technologie Supérieure, Canada


Herve Lombaert is Associate Professor of Computer Engineering at ETS Montreal and holds a Canada Research Chair in Shape Analysis in Medical Imaging.  His research interests focus in Statistics on Shapes, Data & Medical Images, using graph analysis with applications in brain and cardiac imaging.  He had the chance to work in multiple centers, including Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), Inria Sophia-Antipolis (France), McGill University (Canada), and Polytechnique Montreal (Canada).  He is also a recipient of the François Erbsmann Prize, a top prize in Medical Image Analysis.