Innovative Projects Realized

Business interns working within cross-functional teams to develop and commercialize AI-powered solutions in the Public Services sector (1)

AltaML builds artificial intelligence (AI)-enabled solutions to business problems. We work with organisations, bringing together their data and domain expertise with our AI expertise, to develop AI solutions that are deployed in their operations. We also commercialize AI-enabled products business via industry-specific ventures, yielding scalability from our investment in the first solution.

Competition for tech talent is fierce, and our talent strategy includes a talent accelerator program, designed to rapidly equip highly qualified individuals with hands-on work experience in applied AI while providing partners with continuous and cost-effective development of AI solutions. AltaML’s AI Lab for Government, also known as GovLab, is a talent accelerator for public service professionals, post-secondary students and recent graduates. GovLab.ai’s mission is to set a global example of how to transform the public sector through applied AI, and is designed to encourage the growth of technical and business AI skill sets that are in high demand across Alberta and around the world.

The project comprises internships in a variety of technical and business roles within our organization. Within the organization, roles include associate machine learning developer, business development associate, communications associate and accounting associate. Together, these associates contribute to the innovation, growth, and development of not only AltaML and our projects, but also the tech industry as a whole. By continuously learning and improving our current processes through research, testing, and strategic planning, we aim to continue being a leader in the tech industry in North America and continue to drive the innovation of our processes, projects, and services.

As AltaML continues its growth, it will be crucial for us to continuously look to innovate and improve our internal processes, to be able to support this scalability moving forward. Our Finance team has recently implemented new tools and technologies (Stampli, NetSuite, ADP Workforce, etc) that aim to help streamline and improve our accounting and finance processes. For the upcoming term(s), the team will look to further understand and utilize these tools to completely transform our processes. The main focuses of this upcoming project/terms will be refining our cash forecasting model in NetSuite, researching and supporting the integration between NetSuite and our bank to receive automatic bank feeds, preparing consolidation templates for future usage, and developing accounting processes and policies for our newly established Venture Fund group within AltaML. By streamlining and improving our internal processes, we will be better suited to take on additional, more complex projects/work in the future, ultimately supporting the ongoing growth of the company as it transitions further into the scale up phase of the business lifecycle.

Assessing complex trade-offs across coastal adaptation alternatives: incorporating urban context, climate uncertainty and stakeholders.

As communities seek to advance their climate adaptation plans, deciding what projects to implement and when poses a significant challenge, particularly given the remaining uncertainties related to climate impacts, as well as dealing with limited resources for project implementation within communities. Using the Coastal Flood Adaptation Strategy of the City of Surrey, the proposed research brings together elements from existing methods to develop a decision support tool to evaluate different sequences of project implementation across multiple climate scenarios. The results could help communities prioritize projects and identify key moments at which implementing other strategies might be needed. The proposed approach could also be used in other areas of climate adaptation planning, such as in stormwater management or dealing with urban heat waves.

The question of adolescent ideality in education

In the last four years, we have seen world youth revolts in the streets, in the home, and in school. The social response, is almost always accompanied by a tone of disbelief. Adults show disbelief with the adolescent’s desire to be plugged in to a device for an unbelievable number of hours. And paradoxically, when adolescents do show interest in their world and take to the streets, many adults wish they would just stay home. My project asks why these accounts of revolt against the adult community matter to adults entrusted with the care of young people and specifically, looks to consider them as a part of a larger problematic: what is ‘unbelievable’ about adolescence for the adult? To do that, I will work with an international focus with a close look at Turkish and Canadian contexts. I will work with a research team at Ozyegin University, Istanbul on an ongoing project about psychosocial perspectives on youth revolts in Istanbul. I will also work on government responses to the youth protests to see how it relates to the difficulty of adult-adolescent relationship.

The impact of federal funding policies & practices on the communities served by federally funded nonprofits and charities

Charities and nonprofits provide essential programs and services and improve the quality of life in communities. However, anecdotally we know that accessing consistent, non-burdensome funding to support these activities is a major challenge for many organizations. Ultimately, the funding nonprofits are able to access impacts the individuals and communities they serve in a variety of ways. There are major gaps in our knowledge about this issue, so this research project will attempt to fill those gaps using a comparative case study approach to evaluate the impact of federal funding policies and practices on (i) the funded nonprofit organizations, (ii) the communities they serve, and (iii) the ability of the federal government to meet the objectives laid out in the funding programs.

Developing A Retrieval-Augmented Generation (RAG) Framework for Enhanced Data Analysis and Insight Generation for Capital Allocation Decision-Making

The proposed project aims to enhance decision-making in capital allocation by integrating different AI techniques into a data processing system. The goal is to develop an intelligence platform that efficiently processes large amounts of financial data, using AI to uncover valuable insights. The program will have different parts that each works on specific tasks, including collecting data, retrieving data, ranking documents, and producing predictions. This approach will enable the customers to make data-driven and informed decisions of capital fund allocation. The expected benefit is an improvement in the organization’s ability to analyze market trends and allocate resources effectively, leading to better financial outcomes and a competitive advantage in the market.

Sheep Industry Resource Assessments for Sustainable & Unique Vegetation Management

Alberta Lamb Producers (ALP) have developed a series of modules on the basics of vegetation management and forage nutrition including unique sheep grazing scenarios. Resources focus on options to use sheep to mitigate and reduce mechanical, fossil fuel and chemical weed control. Successful early adopter producers have utilized sheep for vegetation management and weed control under various scenarios that include grazing of solar farms, urban/city parks, public lands, forests, and powerline cut blocks. Such grazing partnerships have allowed ALP members to source alternative and new feed sources for their livestock as well as increase animal marketing and rural economic opportunities. This project requires an intern to oversee and lead the launch of the curriculum to industry stakeholders. Creation of a focus group, surveys of students, surveys of case study flocks and land/business stakeholders will be evaluated to make improvements to the course materials and dissemination techniques.

Trilingual students in crossfire: Plurilingualism and identities of the teenage foreign language learners at a ChaoXianZu [ethnic Korean] public high school in Northeast China

This study is a pilot project focuses on examining current issues concerning multilingualism and third language learning, as an initiative to rethink Foreign Language (FL) education in the 21st century. Its main goal is to understand the politics of multilingualism, language investment and identities of students in today’s dynamic classrooms. For a comparative purpose, the study looks into the situations of two groups of first year university students who are in a variety of transitions, especially the ones learning English or Japanese as a new foreign language (FL) or their third language (L3), at a main university located in the ethnic Korean Autonomous Prefecture, northeast China. The study should provide significant implications to the educators and program coordinators, for instance, by reporting the key findings of what does multi-/pluri-lingualism mean to the multilingual learners and their teachers, and how they use multiple (linguistic and media) resources in their FL learning/teaching.

Practical applications of AI in sustainability

Perovskite solar cells are a specific type of solar cell that has become popular in recent years due to their low making cost and higher ability to produce electricity from sunlight. My research project aims to train a neural network that can help predict different characteristics of a perovskite solar cell, thus helping to increase the amount of electricity it can produce from sunlight. A neural network is a method in artificial intelligence that teaches computers to process data in a way the human brain functions. By using neural networks, we can produce accurate predictions on various characteristics(power conversion efficiency, fill factor, open-circuit voltage, etc.) of a perovskite solar cell by just entering data and graphs. It largely facilitates our research on improving the perovskite solar cell to produce more electricity. By obtaining an improved perovskite solar cell, we can use more solar energy in our lives instead of non-renewable energies, thus obtaining a greener and more sustainable future.

Development of in-vitro models to investigate the intestinal lymphatic uptake of drugs

We aim to improve drug delivery by addressing a lack of accurate models predicting how drugs reach the body’s lymphatic system. Our innovative in-vitro models focus on understanding how drugs travel through the intestinal lymphatics. Using chylomicrons, lipid-based vesicles, our models simulate the journey of drugs from the intestines to the bloodstream. This success of this project promises valuable insights for drug development and regulatory assessments, ultimately improving treatments. The partner organization will gain valuable insights and the latest academic knowledge through our collaboration and this newfound knowledge can boost their ability to develop better products and might even speed up the process of getting approval for their products.

Graphene-based quantum materials for environmental applications

This Mitacs Globalink Research Award will support a research collaboration involving Imen Hemmedi, a PhD student working in the group of Dr. Nabila Bitri at the Ecole Nationale Supérieure d’Ingénieurs de Tunis and Prof. Jean-Michel Ménard at the University of Ottawa. The project focuses on leveraging the unique properties of quantum materials to explore innovative techniques addressing timely challenges in the fields of environmental monitoring and pollution control. This internship is perfectly aligned with the intern’s previous expertise in graphene-based materials and thin film fabrication and builds on advanced material characterization infrastructure at uOttawa to pursue a scalable and economically viable solution to environmental issues.
The interdisciplinary project combines thin film fabrication of quantum materials and their use in gas sensing and photocatalysis processes. A scalable spray pyrolysis deposition technique for graphene oxide and reduced graphene oxide will be implemented to produce gas sensors with high sensitivity and unique selectivity properties. We will also study the chemical storage capacity of these graphene-based materials, hence supporting Canada’s net-zero emissions goal. A time-resolved terahertz spectroscopy technique will be used to quantify the performances of these quantum materials for applications in environmental remediation.

Investigation of the role of convective velocities for turbulent spectrum reconstruction

Turbulent flows are complex patterns of fluid motion commonly encountered in nature and engineering applications. Turbulence involves various spatial and temporal scales of motion, making it challenging to measure accurately. This project aims to understand the nonlinear, multi-scale dynamics of turbulence. By utilizing an advection-based method, this work proposes a technique to enhance the temporal resolution of experimental flow measurements. This requires the generation of benchmark experimental Particle Image Velocimetry (PIV) datasets to obtain detailed information about the flow’s dynamics and energy transfer mechanisms. Using space-time correlations, a method will be developed to estimate the scale-dependent velocity of the turbulent structures for use in the proposed advection model. This research has broad implications, from advancing fundamental science in turbulence to practical applications like improving wind energy models. Ultimately, the project contributes to cleaner energy solutions and improved engineering practices.

Towards a predictive understanding of glass toughening by crystalline inclusions

Ion-exchanged or chemically tempered glass such as Corning’s Gorilla glass was a break-through in the design and production of high toughness glass. However, the pace of improvements brought in by this technology is levelling off. Recently, the industry has turned to another approach to product tough glass: the addition of small crystalline inclusions. However, this brings new challenges in predicting the impact of such inclusions on the mechanical and optic properties of materials.
The goal of this project is to conduct computational studies of the impact of crystalline inclusions on the effective fracture properties of such glasses. We will follow the formalism developed in (Hossain et al. 2014) and phase field models of fracture (Bourdin et al., 2001, Bourdin et al., 2008) to perform numerical simulation to assess how density, geometry, and size of inclusions affect the macroscopic fracture behaviour of glass.