Rapid and accurate detection and classification of pathogens (viruses vs. bacteria) is a global challenge affecting healthcare, public health, and environmental safety. Current methods like PCR and CRISPR are accurate but slow, expensive, and require centralized labs and trained personnel. Rapid antigen tests are faster but lack accuracy and cannot differentiate between bacterial and viral […]

In light of the recent concerning growth of cyberattacks in pace, scale, and sophistication empowered by contemporary Artificial Intelligence (AI) techniques with multiple complex hacking and penetration tools, Small and Medium Businesses (SMBs) suffer continuous and serious technical, financial, and societal hits as severe damages of these attacks. This surge in cyberattacks boosted by AI […]

This project aims to explore whether brain tissue deformation, observed through a surgical microscope, can be used to estimate instrument-brain contact forces during epilepsy surgery. Two interns will work on key aspects: one will develop computer vision techniques to track tissue deformations in real time, while the other will create machine learning models to translate […]

This project explores how AI can create expressive, stylized portraits while keeping a person’s identity and emotions intact. By developing advanced AI models, it will help artists, enhance digital content creation, and support industries like entertainment, advertising, and cultural heritage. The technology can be used in museums for interactive exhibits, in film and gaming for […]

L’industrie minière canadienne joue un rôle clé dans l’économie nationale, représentant 5 % du PIB et employant directement plus de 400 000 personnes. Cependant, les arrêts non planifiés des broyeurs SAG Mill entraînent des pertes économiques considérables, atteignant plusieurs milliers de dollars par heure. Ce projet vise à répondre à ce défi en développant une […]

This research project focuses on using advanced machine learning techniques to better predict stock prices, specifically targeting stocks from the S&P 500. By combining powerful deep learning methods—such as LSTM and GRU networks—with adaptive attention mechanisms inspired by Transformer models, the project aims to create forecasting systems that can dynamically adapt to changing market conditions, […]

The past decade has revolutionized the astronomical observation of black holes. In 2015, the gravitational waves generated by the merger of two black holes were detected, which confirmed the predictions of general relativity and led to a Nobel prize. In 2019, an array of telescopes spread across the globe took the first image of a […]

The proposed project aims to develop a Quantum Convolutional Neural Network (QCNN)-based approach to detect vulnerabilities in smart contracts, which are critical components of blockchain technology. By leveraging quantum machine learning techniques, the project seeks to enhance the accuracy and efficiency of identifying security threats in smart contracts, such as reentrancy attacks and integer overflows. […]

This project aims to enhance the annotation of small nucleolar RNAs (snoRNAs) in the Rfam database, a key resource for classifying non-coding RNAs (ncRNAs). Rfam groups ncRNAs into families based on evolutionary relationships, using initial manual curation followed by a computational pipeline developed by the Rfam team. SnoRNAs are ncRNAs that guide RNA modification and […]

Standard medical imaging techniques used to diagnose and inform treatment of spinal conditions include X-Ray, computed tomography (CT), and magnetic resonance imaging (MRI). These images neglect to capture the in vivo dynamic behavior of the spine during activities of daily living, since patients remain as still as possible to capture clear image. In 2023, 20% […]

The proposed MITACS GRA project is part of the Human Surgeome Project at the German Cancer Center, which uses advanced machine learning and deep learning technologies to improve surgical practices. By analyzing large amounts of surgical video data, the project aims to enhance surgical training, skill assessment, and workflow. It will develop systems that provide […]

Bond dissociation enthalpy (BDE) is a fundamental chemical property for predicting molecular stability and reactivity. BDEs are crucial for understanding antioxidant efficiency, enzyme catalysis, surface functionalization chemistry, and drug discovery. This project will focus on predicting BDEs for C-O and C-C bond types in lignin-derived molecules, essential for efficient lignin decomposition processes in biofuel production […]