Representations are fundamental to Artificial Intelligence. Typically, the performance of a learning system depends on its data representation. These data representations are usually hand-engineered based on some prior domain knowledge regarding the task. More recently, the trend is to learn these representations through deep neural networks as these can produce significant performance improvements over hand-engineered […]

A smart anthropomorphic toy is defined as a device consisting of a physical toy component in a humanoid form that connects to a computing system with online services through networking and sensory technologies to enhance the functionality of a traditional toy such as Mattel’s Hello Barbie and Cognitoys Dino. Many studies found that anthropomorphic toys […]

Schizophrenia is a chronic mental disorder associated with a significant health, social and financial burden, not only for patients but also for their families, and society. However, the current treatment methods have been only partially successful, mainly due to the inter-individual differences between patients, which means that a treatment that is successful for one patient, […]

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Graphics Processing Units (GPUs) are increasingly used to accelerate applications and to reduce their energy use. GPUs are particularly attractive for mobile platforms, where battery life is important. However, GPUs are hard to use, requiring developers to apply optimizations to their code to realize the performance and energy benefits of GPUs–a tedious and error prone […]

The fellowship mainly investigates an analysis of the state-of-the-art approaches, design and implementation of cutting-edge deep neural network models to be used on a mobile platform. It explored ways to optimize the deployment of these machine-learning models for prediction tasks on the mobile devices which requires energy efficiency and accuracy.

Rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning […]

As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, […]

Financial markets today are monitored and controlled by artificial intelligent algorithms. Developing these artificial intelligent algorithms requires a large amount of testing against complex patterns and phenomena observed in stock market. The main objective of the proposed project is the development of a market simulator. This is highly challenging and yet promising direction that will […]

Financial markets today are monitored and controlled by machine learning algorithms. The primary objective of this project is to further develop the algorithm for financial market analysis and prediction that the partner possesses at the moment. The algorithm currently demonstrates high accuracy, subject to certain constraints, among which: a small time interval between a prediction […]

Title of the project: Exploring document navigation and interaction on mobile devices Sponsor organization: PDFTron Systems Inc. Intern: (AMY) Xiao-Ming LI Supervisor: Hao (Richard) Zhang School of Computing Science, Simon Fraser University Description: PDFTron Systems Inc. specializes in PDF, SVG, XPS and other graphics technologies. Based on the success of its product – PDFNet SDK […]