type of treatment they will provide to patients. With technological improvements and the availability of a significant volume of data, it is increasingly difficult for care providers to properly evaluate and analyze the options available to them. The current health condition of the patient–reflected in the monitored observations which are recorded in EMR–may depend on […]

Graphics Processing Units (GPUs) are usually employed to quickly render images on everyday computer screens, and do so quickly and efficiently for relatively little cost compared to the use of Central Processing Units (CPUs) that make up the “brain” of the computer. Modern GPUs are able to do hundreds or thousands of simultaneous calculations; rewriting […]

The central problem of pharmaceutical research is to understand the effect of a certain molecule on human or animal biology. Many machine learning models have been developed in recent years and show great efficiency and accuracy for this kind of predictions. However, a problem common to many of the best algorithms is that they require […]

Cette recherche porte sur un sujet appartenant au domaine de l’ingénierie des logiciels et utilisera des techniques du domaine du machine learning. Je travaillerai sur la création des diagrammes de structures dans le langage UML qui est un diagramme représentant la structure interne de classes et les relations entre elles. Le but de cette recherche […]

Optimizing plant processes is of prime importance now more than ever. With stricter infrastructures being placed on safety, environmental effect, and corporate social responsibility, more complex systems that optimize these factors are needed. These complex systems with advanced algorithms are intended to further streamline the existing process while mitigating issues leading to a safer workplace, […]

We’d like to address the issue of 3D reconstruction from 2D images. This means developing a machine learning algorithm that can take a regular photo as an input and generate a full 3-dimensional reconstruction of the contents of the photo. Such technology can be used creatively or to help the coming generation of robots better […]

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 […]

Cameras apply a lot of processing on the raw image recorded by the sensor to enhance the brightness, contrast, and colors, and make the output image visually pleasing. The image is also finally compressed to make the file size smaller. These operations make it difficult to reverse to the raw sensor image which is necessary […]

For this project, we will develop the statistical theory to support, as well as a full functioning prototype for, a large-scale multidimensional data visualization application. This application, which will be fully scalable, will include an interactive user dashboard with customizable widgets. By taking a methodological approach to research, along with an agile approach to development, […]

Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. Recently there has been much interest in application of such technologies to improve agricultural practices. The sensors that are installed in the field transmit real time data regarding numerous environmental variables […]

Our long-term agenda associated with this two year proposal is to address the needs of researchers working with large-scale scientific applications through our contributions to highly parallelized system infrastructure software. At the application level, we are targeting real-world projects such as the computational demands associated with the Pacific Institute for Climate Solutions and NEPTUNE Canada. […]

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 […]