Innovative Projects Realized

Étude de cas : Le rôle de l’intermédiaire GoExport dans l’internationalisation des PME québécoises au Brésil

Le projet intitulé « Étude de cas : Le rôle de l’intermédiaire GoExport dans l’internationalisation des PME québécoises au Brésil » a pour but de déterminer comment des agents intermédiaires comme l’entreprise montréalaise GoExport permettent à des PME québécoises de surmonter les distances qu’elles peuvent rencontrer lors de leur processus d’internationalisation au Brésil. Ces distances sont d’ordre culturel, économique, administratif, technologique et géographique, et peuvent être extrêmement difficiles à surmonter pour des PME ayant peu de ressources à investir dans leurs exportations. Ce projet permettra donc à l’entreprise partenaire GoExport de justifier sa pertinence dans ce processus d’internationalisation auprès de futurs clients.

Governing Networks, Forced Migration and Precarious Housing in the City

The “local turn” of migration policies has been more pronounced since the “refugee crisis” of 2015. Its accompanying camps, emergency shelters, and buildings occupation have put cities under the microscope, underlying the multiple interactions between homelessness and forced migration, including precarious housing and hidden forms of homelessness. Several types of networks are engaged in tackling homelessness and precarious amongst migrant populations. While social, family or ethnic networks might alleviate the risks for more visible forms of homelessness, network members often lack resources to deal with housing and living costs in general (Hermans et al., 2020). In some cases, in particular unaccompanied minors, little or no support network is available, and many depend on the advocacy of neighbours’ collectives close to encampments (Coutant, 2018). Finally, cities themselves are engaged in local, national and transnational city networks aiming at sharing practices, leveraging resources and strategizing advocacy (Spencer, 2022). This project aims to look at exploring the construction and mobilization of these networks as well as analyze their impacts.

Impact of videos about UN SDGs on learning Ukrainian

The project “My UKRainian World” aims to create, pilot test and revise educational video resources for learners and speakers of Ukrainian, whose first or primary language is not Ukrainian. It will attempt to bring Ukrainian language video resources on par with other international languages.

The current phase of the project consists of: an analysis of the feedback data collected from beginner level Ukrainian learners, revision of already created videos during the GRI term, analysis of the structure of voice recordings of the videos in order to obtain datasets for simple Ukrainian language usage, researching and implementing at least one tool to use the acquired dataset in order to create Ukrainian language extension for digital products and preparation for knowledge mobilization.

The intern will be enrolled into all the above objectives of the project during his visit to University of Alberta.

Artificial intelligence for the prediction of variables in health

Today, thanks to microelectronics, it is possible to find technology that continuously collects data such as motion kinematics or physiological data. This type of technology is commonly referred to as wearable and therefore the scope of this project is to find methodologies based on artificial intelligence for the creation of models that can predict a variable of interest. These predictive models are of interest in sports and in general in human health that will allow to follow up the physical condition of a person, estimate the appearance of an injury or determine variables of interest without the need to invest in high technology, among other advantages. The objectives are: develop a software interface that allows the capture of kinematic, physiologic and performance variables from wearable sensors, conceive a software based on machine learning that allows the prediction of kinematic, physiologic and performance variables from data collected and evaluate the robustness and precision of different machine learning techniques.

Performance Analysis of Northern Infrastructure Affected by Climate Change

In this project, considering the rate-dependent behavior of permafrost, a multiphysics-based hazard ??assessment tool will be developed to investigate the life-cycle performance of infrastructure subject ?to ?different seasonal climate projections and climate change scenarios. For this purpose, first, an ?advanced thermo-elasto-viscoplastic (TEVP) constitutive model is being developed based ?on the ?framework of critical state soil mechanics to thoroughly capture the thermal creep ?behavior of ?permafrost. The TEVP constitutive model can thoroughly capture ?the thermal creep behavior of ?permafrost and the rate-dependent deformation of degrading ?permafrost. Afterward, the ?constitutive model will be implemented into a Finite Element (FE) ?software called DISROC to develop a ?multi-dimensional THM numerical platform that can be used as a robust hazard ?assessment tool in the ?life-cycle performance assessment and climate-resiliency analysis of ?northern infrastructure (physical ?asset) ?in permafrost regions.?

UnACoRN: Understanding Affirming Communities, Relationships, and Networks

In recent years, numerous jurisdictions in the USA and Canada have enacted bans on ‘conversion’ practices, i.e., organized attempts to suppress Two-Spirit, lesbian, gay, bisexual, transgender, queer (2S/LGBTQ+), and other minoritized sexual and gender identities. In 2022, our team conducted the UnACoRN survey, a first-of-its-kind binational survey of 9,679 youth (15-29 years of age). Through this project, we are beginning to appreciate the full range of settings and practices that threaten 2S/LGBTQ+ identities, despite ‘conversion’ practice bans and other policy advances to protect the rights of 2S/LGBTQ+ people. Leveraging collaborations between SFU, Vanderbilt, and other North American institutions, we will analyze and disseminate findings in 2022-23, aiming to support 2S/LGBTQ+ health equity strategies. Results will also be used to inform more inclusive and comprehensive approaches to sex education, organized team sports, and places of faith. Finally, UnACoRN data will shed light on where and how we can use the federal ban to educate parents/caregivers, youth, and those in authority to understand the harms of anti-2S/LGBTQ+ messages and contribute to health equity for 2S/LGBTQ+ populations.

Novel cellulose-based membranes for CO2 filtration and ion exchange in aluminum/air batteries

Climate change due to CO2 emission as a result of burning fossil fuels has become an urgent environmental
concern. Moving towards developing clean and sustainable energy sources is inevitable. Expansion of
electrification in the energy sector is one of the effective approaches to developing cleaner and more sustainable
energy sources. Metal-air batteries that are assembled from a metal anode and an air-breathing cathode in a
proper electrolyte, are very promising candidates for clean energy substitutes. AlumaPower is one of the leading
companies in the commercialization of metal-air batteries in Canada. To meet the prerequisites of
commercialization, nevertheless, there are still many technical challenges that need to be addressed. Our
proposal is seeking key solutions that will lead AlumaPower to its goal and has planned an innovative and
comprehensive research project for implementing them. Our solutions are mainly based on developing and
optimizing the performance of advanced functional materials to improve the energy density and service life of
metal-air batteries. Developing such materials will help AlumaPower to consolidate Canada’s role as an important
player in the clean-energy industry.

Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation

Using a fisheye head-mounted camera to estimate human pose in 3D has become increasingly popular in recent years due to its ability to capture activities in unconstrained environments. Egocentric 3D human pose estimation (HPE) has a number of challenges due to self-occlusions and strong distortions. Intermediate heatmap-based representations have been found to be effective in reducing distortion, however self-occlusion remains a challenge, and is the proposed focus of this internship. The goal of the project is to build on previous work, the Ego-STAN project, to improve the performance of that method in the context of 3D HPE, particularly to make it more robust to occlusion. The broader goal is to have a method suitable for cutting-edge motion tracking applications such as activity recognition, surgical training, and immersive XR applications.

Business interns within cross-functional teams to develop and commercialize AI-powered solutions – Part 2

AltaML is an innovative company capitalizing on a major technological trend: artificial intelligence (AI) technologies, enabled by big data, are driving a fourth industrial revolution. AI will transform all industries, but traditional industries face challenges in implementing AI. AltaML has a unique business model to overcome barriers to adoption of AI solutions by industry, which is to bring the innovating startup together with the large organization, thereby bringing together rich datasets, AI talent with a playbook for industry application and the close collaboration of subject matter experts and AI experts–with a mindset for change. In addition to AI expertise, we bring agility that our large, corporate partners often lack, and which is so essential for innovation. With a strategic focus on AI adoption and product, AltaML works across industries as well as with the public sector using a co-development approach to create applied AI solutions as well as joint AI ventures. This rich, complex multisectoral environment provides the breadth that enables insights in one area to be applied in new areas, leading to ever increasing opportunities for innovation.

The project comprises internships in a variety of technical and business roles, which are: associate machine learning developer, business development associate, communications associate, finance associate, associate business solutions consultant, and project delivery associate. Outcomes will include algorithm creation and deployment, data visualizations, market research reports, sales collateral, competitive landscape analysis, feasibility analysis; key messages and content writing such as case studies and feature articles, financial model development, data analysis, financial reports and variance analysis, customer workflow mapping, business case reports, resource allocation plans, project update reports, and project plans.

Plannification des horaires des agents enfonction des prévisions des arrivées d’appelsdans les centres d’appels d’Hydro-Québec

La gestion des opérations de centre d’appels d’Hydro-Québec est une tâche très complexe qui implique souvent un équilibre entre des objectifs contradictoires. En effet, les gestionnaires ont pour but d’atteindre des niveaux élevés à la fois en termes de qualité de service et d’efficacité opérationnelle. La qualité du service est généralement mesurée par les principaux indicateurs de performance cibles tels que le temps moyen d’attente des appelants. L’efficacité opérationnelle est généralement mesurée par la proportion de temps durant laquelle les agents sont occupés à traiter les appels. On peut facilement voir que des niveaux élevés de qualité de service sont associés à de faibles niveaux d’efficacité opérationnelle, et vice versa. Le but de ce projet est d’offrir une aide logicielle aux gestionnaires de centres d’appel lors de la conception des emploi du temps des agents, et ceci en fonction de prévisions à long terme de la demande future entrante (ie, les volumes d’appels futurs)

Machine learning developer interns within cross-functional teams to develop and commercialize AI-powered solutions – Part 3

AltaML is an innovative company capitalizing on a major technological trend: artificial intelligence (AI) technologies, enabled by big data, are driving a fourth industrial revolution. AI will transform all industries, but traditional industries face challenges in implementing AI. AltaML has a unique business model to overcome barriers to adoption of AI solutions by industry, which is to bring the innovating startup together with the large organization, thereby bringing together rich datasets, AI talent with a playbook for industry application and the close collaboration of subject matter experts and AI experts–with a mindset for change. In addition to AI expertise, we bring agility that our large, corporate partners often lack, and which is so essential for innovation. With a strategic focus on AI adoption and product, AltaML works across industries as well as with the public sector using a co-development approach to create applied AI solutions as well as joint AI ventures. This rich, complex multisectoral environment provides the breadth that enables insights in one area to be applied in new areas, leading to ever increasing opportunities for innovation.

The project comprises internships in a variety of technical and business roles, which are: associate machine learning developer, business development associate, communications associate, finance associate, associate business solutions consultant, and project delivery associate. Outcomes will include algorithm creation and deployment, data visualizations, market research reports, sales collateral, competitive landscape analysis, feasibility analysis; key messages and content writing such as case studies and feature articles, financial model development, data analysis, financial reports and variance analysis, customer workflow mapping, business case reports, resource allocation plans, project update reports, and project plans.

Machine learning developer interns within cross-functional teams to develop and commercialize AI-powered solutions – Part 2

AltaML is an innovative company capitalizing on a major technological trend: artificial intelligence (AI) technologies, enabled by big data, are driving a fourth industrial revolution. AI will transform all industries, but traditional industries face challenges in implementing AI. AltaML has a unique business model to overcome barriers to adoption of AI solutions by industry, which is to bring the innovating startup together with the large organization, thereby bringing together rich datasets, AI talent with a playbook for industry application and the close collaboration of subject matter experts and AI experts–with a mindset for change. In addition to AI expertise, we bring agility that our large, corporate partners often lack, and which is so essential for innovation. With a strategic focus on AI adoption and product, AltaML works across industries as well as with the public sector using a co-development approach to create applied AI solutions as well as joint AI ventures. This rich, complex multisectoral environment provides the breadth that enables insights in one area to be applied in new areas, leading to ever increasing opportunities for innovation.

The project comprises internships in a variety of technical and business roles, which are: associate machine learning developer, business development associate, communications associate, finance associate, associate business solutions consultant, and project delivery associate. Outcomes will include algorithm creation and deployment, data visualizations, market research reports, sales collateral, competitive landscape analysis, feasibility analysis; key messages and content writing such as case studies and feature articles, financial model development, data analysis, financial reports and variance analysis, customer workflow mapping, business case reports, resource allocation plans, project update reports, and project plans.