Learning from relational data is crucial for modeling the processes found in many application domains ranging from computational biology to social networks. In this project, we propose to work on developing modeling techniques that combine the advantages of the approaches found in two fields of study: Machine Learning (through graph neural networks) and Statistical Learning […]

In a cognitive wireless mesh network, licensed users (PUs) may borrow surplus spectrum from other PUs and rent them to unlicensed users (SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs, while that of the SUs is to meet their requirements and getting a […]

Any physical processes that we aim to study in space have probably been influenced at some point by the process of star formation. Whether it be the interstellar gas and dust, the formation of clusters of stars, or galaxy evolution, star formation plays a role as a strong driving force for the physics behind these […]

Knowledge graphs store facts using relations between pairs of entities. In this work, we address the question of link prediction in knowledge graphs. Our general approach broadly follows neighborhood aggregation schemes such as that of Graph Convolutional Networks (GCN), which in turn was motivated by spectral graph convolutions. Our proposed model will aggregate information from […]

Automatically assessing a pilot performance during a flight training session is a capability that can enhance the flight instructor during his duty. From data gathered during a flight maneuver, we are looking for a way to automatically assess pilot performance to augment instructor performance and provide objectivity during flight training assessment.

This fundamental research project investigates semantic visual navigation tasks, such as asking a household robot to “go find my keys”. We seek to enhance the efficacy of repeated search tasks within the same environment, by explicitly building, maintaining, and exploiting a map of locations that the robot had previously explored. We also seek to exploit […]

API usability specifies how easy, efficient, error-preventing, and pleasant an API of a software library is from its users’ perspective. With machine learning (ML) techniques becoming increasingly powerful and pervasive, many non-programmers and casual users (e.g. domain experts in medicine or geography) started to explore ML libraries. However, many find them challenging to use because […]

In this project we aim to automatically learn an augmenter network by using an approximate online bilevel optimization procedure. We plan to learn a augmenter network that generates a distribution of transformations that minimizes the loss on a validation set. By unfolding the gradients of the training loss, we will optimize the loss on validation […]

The research project entails finding a method to detect corroded steel wires used in the manufacturing of tires at Michelin in Bridgewater. This would be achieved using methods that are proven safe for humans and the environment. Also, the detection equipment will be integrated in the existing Michelin facilities in Bridgwater, NS, Canada.

This project aims at investigating the effect of communication constraints on cooperative manipulators. Afterward, the communication constraints will be detailed modeled, based on that the control strategy specifically for the multi-robot system will be developed. The stability of the system under the designed control strategy should be guaranteed, and the performance regarding the execution of […]

The Mobile Robotics and Autonomous Systems Laboratory of Polytechnique Montréal is specialized in drone development and applications. The intern will join the team in charge of developing a payload transport system with drones. IN this research work we will develop a simulation environment, validate the different control strategies in the simulation environment and implement the […]

Machine learning (ML) has recently achieved impressive success in many applications. As ML starts to penetrate into safety-critical domains, security/robustness concerns on ML systems have received lots of attention lately. Very surprisingly, recent work has shown that current ML models are vulnerable to adversarial attacks, e.g. by perturbing the input slightly ML models can be […]