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Dominique Beaini

Associate Industry Member
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Head of Graph Research, Valence Discovery
Research Topics
Graph Neural Networks
Learning on Graphs
Molecular Modeling
Multimodal Learning
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Biography

I am currently a research unit team lead at Valence Discovery, one of the leading companies in machine learning applied to drug discovery. I am also an adjunct professor at Université de Montréal, in the Department of Computer Science and Operations Research (DIRO). My goal is to push the state of machine learning toward a better understanding of molecules and their interactions with human biology. I completed my PhD at Polytechnique Montréal in the area of robotics and computer vision.

My research interests are graph neural networks, self-supervised learning, quantum mechanics, drug discovery, computer vision and robotics.

Current Students

Majdi Hassan
PhD - Université de Montréal
Co-supervisor :
Kirill Neklyudov
Website
Github
Google Scholar
Ethan Kreuzer
Master's Research - Université de Montréal
Jungyoon Lee
Master's Research - Université de Montréal

Publications

Saliency Enhancement using Gradient Domain Edges Merging
Dominique Beaini
Sofiane Wozniak Achiche
Alexandre Duperre
Maxime Raison
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the b… (see more)oundaries of an image and salient object detection which finds the important object in an image. This progress happened thanks to the rise of deep-learning and convolutional neural networks (CNN) which allow to extract complex and abstract features. However, edge detection and saliency are still two different fields and do not interact together, although it is intuitive for a human to detect salient objects based on its boundaries. Those features are not well merged in a CNN because edges and surfaces do not intersect since one feature represents a region while the other represents boundaries between different regions. In the current work, the main objective is to develop a method to merge the edges with the saliency maps to improve the performance of the saliency. Hence, we developed the gradient-domain merging (GDM) which can be used to quickly combine the image-domain information of salient object detection with the gradient-domain information of the edge detection. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of the F-measure of at least 3.4 times higher on the DUT-OMRON dataset and 6.6 times higher on the ECSSD dataset, when compared to competing algorithm such as denseCRF and BGOF. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
2020-02-11
ArXiv (preprint)
arxiv.org
arxiv.org
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso
Luca Cavalleri
Dominique Beaini
Pietro Lió
Petar Veličković
arxiv.org