Projets novateurs réalisés

Understanding Statistics through Physically Manipulating Data

The rise of Big Data, social networking, and mobile interactions coupled with an accelerating increase in the amount of structured and unstructured information enabled by cloud based technologies is forcing organizations to focus on information that is most relevant, value-generating, and risk-related. Problems arise when we make decisions based on good enough metrics (e.g. means) instead of proper statistical methods (e.g. T-tests, ANOVA). The issue is that everyday business workers need to understand statistics in a cloud centered world. In this proposal we hypothesize that data that is animated through physical manipulation (e.g. metaphors such as `pulling out data’, ‘massaging the data’ etc.) will result in improved understanding of statistical methods and greater acceptance of such methods in the workplace. Our research is primarily based off the successful presentations of animated statistics by Hans Rosling at TED [9]. We propose to build upon this successful teaching technique by further incorporating commonly used physical manipulation metaphors to facilitate statistical data analysis

Attitudes féministes et image corporelle au sein de la dyade mère-fille : le rôle de l’auto-compassion

En tant que mère, il peut être normal de se sentir impuissante face aux préoccupations corporelles des adolescentes. Plusieurs parents veulent aider leur fille à développer une bonne image corporelle, mais ne savent pas quoi dire ni faire concrètement pour y parvenir. En collaboration avec la Fondation Jeunes en Tête, dont la mission est de prévenir la détresse psychologique chez les jeunes, nous souhaitons mettre sur pied une boîte à outils afin de soutenir les mères dans le développement d’une image corporelle positive chez leur adolescente. Notre projet vise le partage d’outils concrets que les mères pourront mettre en place pour aider leur fille à se sentir bien dans leur corps. L’objectif de notre boîte à outils est de susciter une réflexion féministe sur les enjeux liés à l’image corporelle afin que les mères puissent transmettre une vision bienveillante et positive du corps à leur adolescente. Par exemple, nous souhaitons aborder des situations du quotidien, comme une séance de magasinage, et outiller les mères sur ce qu’elles peuvent faire pour soutenir positivement l’image corporelle de leur adolescente à ces moments.

L2M – Carbon Dots of Asphaltenes Origin for Friction Reduction

My technology introduces AsphaDots, a lubricant derived from asphaltenes-based carbon dots. This innovative product aims to address friction and wear issues in steel and related materials, commonly found in sliding, rolling, or contact interfaces in industrial machinery, as well as in natural and biological systems. Water-based lubricants have become alternatives to petroleum-based lubricants in a range of industrial applications. There is great need to solve this problem because friction and wear in moving mechanical systems have negative effects on durability, environmental compatibility, and efficiency. Most commercial lubricants are petroleum-based with inherent environmental concerns, difficult to dispose waste (used) lubricants, and easily attract and retain dirt and wear particles. The increasing scarcity of petroleum resources and growing environmental concerns have prompted the development of alternative solutions for friction reduction.
The key concept in dealing with these friction, wear, and attendant environmental problems is the replacement of petroleum-based lubricants with water-based analogues like AsphaDots. Water offers several advantages as a lubricant including low cost, abundance, environmental friendliness, effective cooling, easy cleaning, and enhanced thermal conductivity. As customers are searching for petroleum-based lubricants alternatives, AsphaDots-based lubricants are non-toxic, cheap, and easily re-usable as their impressive hydrophilicity allows filtering of wear particles and dirt in waste AsphaDots lubricants.
Two companies have already shown interest in the technology. Currently, 10 g AsphaDots per batch can be prepared in our laboratory. And we hope to develop a prototype that enables the preparation of 10 kg per batch.

L2M – A light-based device and accompanying App for breast cancer screening based on machine learning

Breast cancer is a leading cause of female cancer deaths, but early detection significantly reduces mortality rates. Annual mammograms are common for women aged 40 and older, but those under 40 often go undiagnosed. False negatives are a concern, especially with dense breast tissue. Many women with detected lesions or genetic risk experience stress during follow-up and have difficulty with self-examinations. Limited access to reliable diagnostic systems increases undetected cancer risk, influenced by ethnicity and socio-economic factors.
We propose a portable, rapid, non-invasive, low-cost, and user-friendly tool for early-stage breast cancer detection. This device helps women familiarize themselves with their breasts. Specialists can also use it for screening and follow-up. The device uses a red-light source to illuminate the breast, with malignant tumors absorbing light and appearing as dark shadows. A mounted camera captures images, which are analyzed using image analysis and deep learning. This semi-automatic approach sends images and analysis to physicians for further evaluation. The App allows for additional services and features over time.
Commercializing such a device poses significant challenges. Medical assistive devices carry high commercial risks, and the business model is complex. The highly competitive medical device market requires extensive market research and thorough data collection, analysis, and integration before manufacturing. This project allows for the development of a business model, focusing on legal procedures and standards for medical device development. Additionally, it involves gathering and analyzing market research data to assess the feasibility and success of commercialization, aiming to find a safe path to market.

L2M – Smart Logistics Optimization System Using Artificial Intelligence

The logistics industry faces significant inefficiencies due to lengthy planning and dispatching re-optimization processes, resulting in delays, increased compensations, and suboptimal transit times. These challenges arise from difficulties in assessing resource status, managing incoming requests, and coordinating communication between dispatchers, drivers, and resources. Our smart logistics optimization system leverages real-time data and advanced Artificial Intelligence algorithms to optimize resource dispatching, thereby improving overall operational efficiency and significantly reducing planning times. By adapting to real-world conditions, our system ensures efficient resource use, minimizing downtime and maximizing productivity. Improved visibility into the supply chain allows for proactive problem-solving, preventing issues before they become significant. This leads to timely deliveries and consistent service quality, resulting in higher customer satisfaction.

L2M – EcoTruck Smart Navigator: An AI-powered software for range map prediction and route optimization for electric trucks

The proposed project, EcoTruck Smart Navigator, aims to develop an AI-powered software solution that helps electric truck fleets optimize their routes and improve range estimation. By refining and testing our prototype, we will provide fleet operators with a tool that reduces travel time and energy consumption, addressing key challenges in the transition to zero-emission vehicles. This project will enhance the partner organization’s operational efficiency, support their sustainability goals, and contribute to meeting regulatory targets for reducing greenhouse gas emissions.

L2M – Lucy DNA Biotechnology

LucyDNA introduces a cutting-edge genetic testing platform specializing in personalized healthcare solutions. Leveraging polygenic risk scores provides comprehensive genetic risk assessments for chronic diseases, offering tailored disease prevention, screening, and treatment recommendations. Targeting hospitals, mature individuals, and health organizations, LucyDNA enhances healthcare planning with precise insights, ensuring optimized health outcomes and precision medicine.

L2M – Biomimetic biointerfaces for high throughput in vitro modeling of gut-pathogen interactions

The recognition of gut microbiota, its interplay with human tissues and cells, and the signaling pathways they could generate in concrete with together, specially once imposed to gastrointestinal medications, encouraged researchers designing and establishing bioengineered platforms, enable to serve as a discovery tool for developing of microbiome-related therapeutics, monitoring medications combating detrimental effects of antimicrobial resistance (AMR) pathogenic species infections or carcinogens on human epithelial cells, or checking probiotics and nutraceuticals benefiting human health. We are introducing a bio-inspired intestinal villus mimicking structure composed of natural extracellular matrix, that primarily adheres human intestinal epithelial cells without any significant cytotoxicity, stimulating tight epithelium formation, cell polarization, and differentiation into strains capable to accumulate dense mucosal barrier potentially functioning as a site for microbial colonization, controlling their invasion into underlaying intestinal cells and verotoxin generation. The established balanced microenvironment for cell-bacteria co-culture brings us the opportunity to monitor the crosstalk among these species in details. In light of this innovative bioengineered material mimicking human body conditions and physiology, the need for in vivo testing for pre-clinical assessments of new drugs and medications is minimized. Traditional methods based on in vivo models, whose translatability to human’s body condition is frequently challenged, are time-consuming and often require skills in animal surgery. The outcomes of this innovation facilitate the rapid screening of gastrointestinal medications, consequently can significantly reduce the burden on the Canadian and global healthcare systems.

Cost-Effective and Intelligent Monitoring of Large-Scale Software Systems

This research project aims to tackle the challenge of ensuring the reliability of large-scale software systems, which are crucial for various applications such as online services and data processing. The project focuses on developing cost-effective and intelligent methods for monitoring these systems to anticipate and prevent runtime incidents, such as crashes or errors, which can disrupt operations and lead to costly downtimes.

Throughout the project, the goals will be achieved through a systematic approach involving data examination, statistical analysis, and machine learning techniques. By analyzing monitoring data and building predictive models, the research will successfully identify patterns indicative of potential runtime incidents, empowering proactive measures to mitigate risks and enhance system performance.

The project provides numerous benefits to all involved parties. For the student, it offers practical experience, skill development, mentorship, networking opportunities, and contributions to academia. The academic supervisors benefit from enriched collaboration, reputation enhancement, knowledge exchange, and potential publication opportunities. Additionally, the host institution gains from the student’s contributions to ongoing research efforts and potential future collaborations.

Improving AI by Learning Directly from Human Preferences

Our project aims to improve how AI models learn from human feedback. Current methods assume human preferences can be reduced to a single “reward” value, but research shows this isn’t always true. We will investigate if an AI algorithm can learn from human preferences without relying on rewards. If possible, we’ll design and test algorithms which learn without reward. If not, we’ll explore the cases where rewards are necessary. This research will promote AI systems that are better aligned with human preferences, benefiting the scientific community and industry sectors like healthcare and education. The insights gained from this project will advance Canada’s AI research and reinforce both Mila and Stanford’s global leadership in AI.

Satellite image enhancement & crop classification

Our research project focuses on monitoring crops using satellite images. To address the problem, we leverage a fast-growing field of graph signal processing (GSP), which expands upon traditional signal processing techniques such as Fourier transform and wavelets to accommodate the graph domain. Specifically, we propose to unroll a designed graph-based algorithm into an interpretable feed-forward neural net for end-to-end parameter tuning, in order to enhance satellite images and classify field crops. The primary benefit for the public is the development of a robust and efficient model for crop monitoring. The model is resilient against the known covariate shift problem, which occurs when the distribution of input data differs significantly from the training and test datasets. Unlike conventional “black box” deep learning models, our model requires much less data for training to train significantly fewer network parameters, enabling an operator to save resources and time. With this technology, an operator can swiftly and accurately assess crop health and productivity, leading to informed decision-making and optimized agricultural practices.

Growing Sustainable Tourism in Edmonton’s Chinatown

Edmonton’s Chinatown has long served as a vibrant cultural, social and commercial hub for generations of Asian immigrants and local residents. Yet it is characterized by a complex blend of social and economic challenges. This proposed project, “Growing a Sustainable Tourism in Edmonton’s Chinatown,” is designed to accelerate efforts to revitalize Edmonton’s Chinatown through planning and developing recreation and tourism products and experiences. This project involves collaborative development of a Tourism Strategy for the neighbourhood that will strengthen community and working bonds amongst stakeholders, document diverse perspectives on the best opportunities for Chinatown to explore regarding tourism investment, and inspire collective action amongst government, NGO, researchers and community members to accelerate roll-out of new tourism offerings.