Maxime Gasse
Polytechnique Montreal
Local AA-5487
Maxime Gasse is a postdoctoral researcher within the Canada Excellence Research Chair (CERC) in Data Science for Real-Time Decision-Making, under the supervision of Profs. Andrea Lodi and Laurent Charlin. Maxime holds a Ph.D. in machine learning from the university of Lyon, France, and is currently interested in investigating how deep learning methods can be used to help solving operational research problems such as mixed integer programs.


Journal papers

M. Gasse, F. Millioz, E. Roux, D. Garcia, H. Liebgott, D. Friboulet, 2017. High-Quality Plane Wave Compounding Using Convolutional Neural Networks. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (TUFFC), vol. 64 (10), pp. 1637-1639, 2017

M. Gasse, A. Aussem, H. Elghazel. A Hybrid Algorithm for Bayesian Network Structure Learning with Application to Multi-Label Learning. Expert Systems With Applications (ESWA), vol. 41 (15), pp. 6755-6772, 2014


D. Garcia, R. Le Goff, M. Gasse, A. Aussem. 2014. Optimal Sensor Locations for Polymer Injection Molding Process. European Scientific Association for Material Forming (ESAFORM), pp. 1724-1733, 2014

Conference papers

M. Gasse, F. Millioz, E. Roux, D. Garcia, H. Liebgott, D. Friboule, 2017.  Accelerating Plane Wave Imaging through Deep Learning-Based Reconstruction: An Experimental Study. IEEE International Ultrasonics Symposium (IUS), Washington, DC, USA, 6-9 Sept. 2017.

M. Gasse, A. Aussem, 2016. Identifying the Irreductible Disjoint Factors of a Multivariate Probability Distribution. International Conference on Probabilistic Graphical Models (PGM), pp. 183-194, 2016

M. Gasse, A. Aussem, H. Elghazel, 2014. On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. International Conference on Machine Learning (ICML), pp. 2531-2539, 2015

A. Aussem, P. Caillet, Z. Klenm, M. Gasse, A-M. Schott, M. Ducher, 2014. Analysis of Risk Factors of Hip Fracture with Causal Bayesian Networks. International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), pp. 1074-1085, 2014

M. Gasse, A. Aussem, H. Elghazel, 2015. An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 58-73, 2012


M. Gasse, 2017. Probabilistic Graphical Model Structure Learning : Application to Multi-Label Classification. PhD Thesis, Claude Bernard University Lyon 1, 2017