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.

Online Business Model Prediction Service

In the business, it is critical to understand and predict needs of customers in advance and in precision. Machine Learning and Artificial Intelligence make it possible to extract the desirable properties and predict the objective in the future. This project is interested in the implementation of this concept as a toolbox. The toolbox will consider Online Business Model Prediction Service (OBMPS) as the objective to create an environment to drive smart decisions.

Study of the Latent Space in NLP: Mathematical Foundation and Application to Disentanglement

Recent progress on word and sentence embeddings has enabled efficient representation and learning of complex high dimensional probability distributions over rich text data. The proposed research aims at addressing some of the fundamental questions in this field: What are the natural mathematical structures on that latent spaces? How to find a meaningful basis? What is the best method of disentanglement for NLP?

Development of machine learning and artificial intelligence toolbox to monitor data center risks and performance

Missing Link Technologies Ltd. provides customized and effective solutions to its partners for their IT infrastructure & operations. They are especially involved with solving the IT facilities management challenges of Telcos and Datacenters. The proposed research in collaboration with Missing Link Technologies Ltd. is aimed at leveraging the advances in machine learning and artificial intelligence in risk management, performance assessment and availability prediction of a data center. A suite of tools and techniques will be proposed towards this end.

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