Machine Learning to Predict Temporomandibular Disorders Risk from Genotypes

The goal of this project is to develop new machine learning methods and computational strategies to mega-analyze data from well-characterized datasets on chronic pain conditions to develop a genetic predictive tool. This tool will be implemented in an online interactive dashboard and used by the Quebec Pain Research Network (QPRN) community.

OFDM radio receiver with Deep Learning

This project involves research in applied artificial intelligence in the field of communications. Using AI, complex building blocks in communication systems are to be simplified and designed in a highly cost-effective manner. The use of AI will allow communications systems become more cognitive in nature and give access to affordable software defined radios. This program would provide the means for the intern to innovate and execute a technology that would not have been possible otherwise.

Assessing the impact of an immersive VR gaming experience on navigation ability and spatial cognition in an elderly population

This project will investigate whether playing an immersive virtual reality (VR) game called DoVille is beneficial to older adults’ memory and navigation abilities. Spatial navigation is a fundamental skill that relies on our ability to make an accurate mental map of the space around us, be aware of our position in the environment, and remember a path through that environment. These abilities are known to deteriorate in older adults, which can lead to a loss of independence and quality-of-life.

Self-Adaptive Pattern Recognition with Deep Neural Networks

The purpose of this project is to investigate self-adaptive forecasting and anomaly prediction algorithms based on deep neural networks (DNNs). DNNs present a compelling technology due to their wide-spread availability through open-source projects (e.g. TensorFlow, MXNet). However, usability of DNNs in scenarios outside of image, speech or text pattern recognition is mostly unproven. This project aims to reduce the knowledge gap that exists in the usage of DNNs in the context of pattern recognition with DNNs in network management and network equipment manufacturing.

Lake Melville Oceanography Study

The research will establish a hydrodynamic numerical model of the forces which exert influences on the circulation and the residence time (amount of time water spends in a given body of water) of Lake Melville, Labrador. The study seeks to understand the water properties in the lake, and how the development of nearby hydroelectric projects and climate change affect the oceanography of the lake.

Security protocols for cloud-based noisy intermediate-scale quantum computers (NISQ): development and implementation.

AgnostiQ Labs is looking to develop immediately applicable encryption/obfuscation techniques for quantum computing. At present, encryption protocols developed in academia are unsuitable for real-world applications because they largely depend on quantum hardware that do not yet exist.

3d density estimation using normalizing flows and its application to 3d reconstruction in cryo-EM

Generative models enable the researchers to address multiple problems spanning from noise removal to generating novel samples with properties of the domain. Generative models are commonly studied for images and in this project the idea will be expanded to 3D structures or volumes. Single-particle cryo-electron microscopy (cryo-EM) is a technique to estimate accurate 3D structures of biological molecules which is used by practitioners in fields like precision medicine. This allows them to design drugs that could cure patients with rare diseases and avoid side effects.

Micro Action Impact Measurement Index for the United Nations' Sustainable Development Goals (aka Project MAI-MI)

In a historic United Nations (UN) summit, world leaders adopted 17 Sustainable Development Goals (SDGs) as a universal call to action to address the global challenges we face by the year 2030, including those related to poverty, inequity, environmental degradation, prosperity, and peace and justice. Together, the UN and their partners have underscored the importance of evidence-based and transparent long-term pathways, in which sound metrics and data are critical for turning the SDGs into practical tools for problem solving, tracking progress and accountability.

A Cross-Cultural Study of Electromyography Input for Older Adults

The proposed research concerns the use of Electromyography (EMG) input, via the Myo Armband, for older adults (OAs). EMG input, such as that available through off-the-shelf products, such as the Myo armband (made by a Canadian firm, Thalmic Labs from Waterloo), enables access to interactive commands in an intelligent home setting. Gestures facilitated by EMG armbands require brisk movements, and require extending our arms/forearms beyond the level of comfort of even the range-of-motion possible by OAs.

Developing Smart Price Forecasting Service for Supply Chain Procurement of Agri-fresh Produce Using Machine Learning

Loblaw Companies Limited (LCL) supplies all fresh produce (FP) to South Western Ontario stores from Waterloo Distribution Center (DC). DC decides prices and quantities to meet FP demand. Timed fair priced orders minimize waste, bring prosperity to growers, consumers and FP trades. Factors affecting prices are highly uncertain due to environmental and socio-economic effects such as income, labor, trade, globalization and climate change which makes price prediction challenging.

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