AI & Data Science
AI & Data Science
AI and Data Science projects explore how intelligent algorithms can analyze data, make predictions, and support decision-making in real-world contexts. Students apply methods such as machine learning, natural language processing, and predictive analytics to develop solutions that are innovative, practical, and responsible.
Ami - Cognitive-Style Adaptive AI Tutor
Ami is an adaptive tutoring system that personalizes learning goals, learning paths, session content, quizzes, and tutoring support around each learner. The current codebase works into a fuller tutoring platform with persistent runtime state, verified-content grounding, adaptive learner modeling over time, and production-style backend/frontend integration. The key capabilities of Ami include goal clarification and skill-gap analysis, adaptive learner modeling, personalized learning paths and sessions, multi-modal learning content, tool-using tutor and quality, safety, and analytics.
This high-fidelity simulation environment, built on NVIDIA Isaac Sim, enables the NSERC research team to perform rapid iteration on vision models for high voltage insulator detection. By leveraging Synthetic Data Generation (SDG) and domain randomization, we can simulate edge-case environmental conditions that are impossible to replicate in the field. This allows for the rigorous validation of collision-avoidance algorithms and autonomous flight controllers within a photorealistic, physics-accurate digital twin before transitioning to physical airframes.
This project focused on designing an AI-powered decision-support system for emergency departments to improve triage efficiency, patient prioritization, and resource allocation. Traditional emergency care workflows often suffer from long wait times, inconsistent prioritization, and limited visibility into operational performance, while existing solutions mainly focus on basic patient classification without supporting real-time clinical actions. The proposed system integrates conversational AI, clinical validation, and operational analytics into a multi-stage decision-support framework. It includes an AI triage agent that interprets patient symptoms through text or voice input, a nurse validation layer that incorporates vital signs for clinical reliability, and a decision-support agent that recommends patient routing and care pathways. An operational dashboard provides real-time insights into patient flow and resource usage across the department. The outcome is an intelligent and explainable triage support system capable of enabling faster, more informed decision-making and improving overall emergency department efficiency.
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DataLens Ottawa is an automated system that evaluates the quality of Open Ottawa datasets, detects key data issues, and presents clear results through a dashboard. It can give feedback from five dimensions, including accessibility, usability, freshness, metadata, and completeness. Then it can provide the relevant automatic suggestions for users on which kind of application they should use, and which is not suitable for the related datasets.
Orion – Surgical AI Assistant integrates artificial intelligence and augmented reality to enhance robotic-assisted surgery. Through AI reasoning, voice recognition, and real-time AR visualization, Orion enables surgeons to access and interpret critical patient data hands-free during operations. The system combines Python (FastAPI), Unity, and WebSocket protocols to synchronize AI reasoning with AR overlays in real time. Designed and tested by three graduate students, Orion demonstrates how human-centred AI can improve surgical precision, safety, and decision-making.
AI-supported platform to enable community-driven lifelong learning (LEAP)
LEAP is a web-based platform designed to support lifelong learning by providing personalized, community-driven education. The platform uses artificial intelligence to generate customized learning plans for users based on their goals, interests, and learning preferences. LEAP enables users to upload educational resources, receive AI-powered recommendations, and explore content that is relevant to their learning journey. Unlike traditional platforms, LEAP focuses on providing flexible, self-directed learning opportunities, catering to individuals with limited time or resources for formal education. The platform encourages community contributions, allowing users to share knowledge and experiences. It fosters collaboration and peer-to-peer learning, making education accessible to all. Through user-friendly features like personalized study plans, content discovery, and a dynamic feedback loop, LEAP aims to address barriers to learning and empower users to achieve their educational goals at their own pace. The platform also ensures scalability and adaptability for future enhancements.
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InvisiFall | ML-Enhanced Radar Fall Detection System for Retirement Homes
The InvisiFall project develops a radar-based fall detection system for elderly care, utilizing FMCW mmWave radar technology to track movements and detect falls in real-time. Unlike traditional wearable solutions, this system is non-intrusive, preserving privacy while ensuring safety. The system uses machine learning algorithms to analyze radar data, trigger alerts, and notify caregivers about falls. It is designed to work in various residential environments, including nursing homes, providing enhanced protection for elderly individuals, particularly those with cognitive impairments. By eliminating the need for wearable devices, InvisiFall addresses the discomfort and risk of falls without compromising user privacy. The system aims to improve response time, reduce fall-related injuries, and increase the quality of care for elderly residents, ultimately making it a valuable tool in senior healthcare management.
E-Hospital | AI-Powered Healthcare Solution for Smarter, Safer Patient Care
The E-Hospital platform redesign focuses on improving the user experience for both doctors and patients by addressing usability issues and enhancing workflow efficiency. For doctors, the platform’s dashboard was redesigned to better reflect clinical routines and minimize cognitive overload. Key features like the patient list, patient overview, and encounter sections were optimized for quicker access to essential information. For patients, the platform simplifies tasks such as booking appointments, reviewing lab results, and requesting prescription refills. The redesign incorporates user-centered design principles to ensure that both patient and doctor interactions are intuitive, efficient, and aligned with their needs. AI features were integrated to enhance decision-making for doctors, while streamlined navigation paths improve task recognition for patients. Extensive usability testing and iterative design ensured the platform’s enhancements meet the expectations of healthcare professionals and patients, ultimately leading to a more accessible and effective E-Hospital platform.
Cell Free Layer Detection using AI
The "Automatic CFL Detection Using AI" project develops a sophisticated application for automatic measurement of Cell-Free Layer (CFL) thickness within blood vessels, which is crucial for understanding blood flow dynamics and the effects of microcirculation on blood viscosity and gas/nutrient exchange. The system leverages Convolutional Neural Networks (CNN) to fully automate the segmentation of CFL in medical images. The application is designed to be fast, user-friendly, and adaptable to varying image quality and sizes, ensuring accuracy across diverse datasets. Key metrics include prediction accuracy, application size, response time, and user feedback, with targets aimed at improving performance and usability. The system incorporates edge detection techniques and advanced semantic image segmentation to provide accurate results, while also allowing for batch processing of images and videos. The solution is designed with ethical considerations around data privacy and inclusivity in mind, ensuring the system is accessible and compliant with medical standards.
AI-Powered Agile Lifecycle Platform
This project focused on analyzing a real-world engineering and organizational challenge and developing practical, data-driven solutions to address identified gaps. Our team worked closely with project requirements and stakeholder expectations to evaluate existing workflows, identify inefficiencies, and propose improvements grounded in research and professional practice. Using benchmarking, structured analysis, and iterative feedback, we translated complex findings into clear, actionable deliverables. The project emphasized usability, clarity, and feasibility, ensuring that proposed solutions could be realistically implemented within operational constraints. Through collaborative teamwork, we produced well-documented outcomes that balance technical accuracy with effective communication. This project highlights the importance of applying engineering problem-solving methods beyond technical design, demonstrating how structured analysis, stakeholder-focused thinking, and clear documentation can contribute to improved decision-making, operational efficiency, and long-term impact in an industry or organizational setting.
PDF INSIGHT ENGINE
The PDF Insight Engine is an AI-powered, privacy-first solution designed to transform how users interact with unstructured PDF documents. Built for industries such as healthcare, finance, legal services, and research, the system enables real-time extraction, summarization, and querying of long and complex PDFs through a conversational, chat-based interface. Using advanced Natural Language Processing (NLP) and Large Language Models (LLMs), the engine accurately parses text, tables, and document structures while allowing users to ask both simple and complex questions. A strong emphasis is placed on data security and ethics, with features such as AES-256 and RSA encryption, secure user authentication, session management using JWT, and non-persistent storage to protect sensitive information. Deployed using a scalable serverless architecture, the PDF Insight Engine significantly reduces manual document analysis time, improves decision-making efficiency, and ensures compliance with data protection standards, offering a secure, intuitive, and intelligent document analysis experience.