The dramatic growth of the Internet has enhanced access to information, fostering seamless communication and promoting effective collaboration. Unfortunately, advanced network traffic control and monitoring systems have empowered state-level actors to deploy large-scale surveillance and censorship mechanisms that track people’s Internet activities or limit their ability to freely access and publish information. Recently, multiple initiatives […]

The use of orbital angular momentum (OAM) for free-space optical (FSO) communications is almost unexplored. The proposed research will investigate the bit-error rate (BER) performance of FSO systems using OAM. The analysis will begin by considering the ideal case of separable OAM-modulated FSO system performance, to be followed by the individual and combined effects of […]

This project will develop novel circuit metrics to predict model performance under realistic noise conditions, offering a practical approach to enhancing QML trainability. The research will investigate optimal parameter resilience across different circuit depths, qubit counts, and problem types, while comparing overparameterized and underparameterized regimes. Additionally, circuit metrics will be developed to predict model performance […]

Machine learning (ML) is transforming industries like IT, finance, and healthcare, but the code that powers these systems is still mostly written and updated by hand. This project explores how Large Language Models (LLMs) can assist developers by predicting and suggesting code edits for ML projects. By analyzing real-world code from public repositories, the research […]

This project uses AI and smart thermostats to predict heatwaves in Canada, helping protect public health. By analyzing indoor and outdoor temperature data, we aim to improve heatwave forecasting and understand how extreme heat affects indoor spaces. Using machine learning, we develop models to predict future indoor temperatures, supporting better planning and response strategies. The […]

This project explores how people from different cultures make important life decisions, such as career choices or relationships. It focuses on understanding how cultural norms, social values, and religious beliefs influence decision-making. The research will use advanced text analysis (Natural Language Processing) and surveys to identify patterns in how people from different backgrounds approach decisions. […]

This project aims to develop advanced machine learning models to analyze arterial blood pressure (ABP) waveforms from patients in intensive care units (ICUs). By using a large dataset and cutting-edge techniques like transformer architectures and contrastive learning, the goal is to create models that can accurately predict patient outcomes, such as ICU mortality and hospital […]

Professor Kim’s team is advancing AIST’s initiative to develop a Foundation Model for Computer Vision, a transformative AI system designed to address diverse tasks through large-scale pre-training. These models are vital for computer vision, which focuses on enabling machines to interpret visual data like images and 3D point clouds. Professor Kim’s work centers on 3D […]

This project will investigate new methods for improving decision-making tools that help organizations make better choices when faced with uncertainty. Specifically, we will study how to more accurately estimate weights in the Analytic Hierarchy Process (AHP), a common tool used for Multi-Criteria Decision Making (MCDM). By focusing on interval and fuzzy weight estimation, the project […]

The proposed project aims to develop a wearable biosensor for real-time monitoring of critical cardiovascular disease (CVD) biomarkers, such as C-reactive protein, Troponin I, and Myoglobin, along with physiological parameters like heart rate. The biosensor will achieve high selectivity and durability by utilizing molecularly imprinted polymer (MIP) technology. The intern will collaborate with Professor Joseph […]

This research project pioneers the development of novel molybdenum disulfide (MoS2)/copper hybrid materials through electrochemical intercalation and exfoliation techniques. By transforming readily available powdered molybdenite—a byproduct of Canadian mining operations—into atomically thin layers with enhanced properties, the work creates a sustainable pathway for producing high-performance two-dimensional materials without organic additives that typically compromise conductivity. The […]

This project aims to enhance the safety, precision, and efficiency of robotic surgical assistants — collaborative robots designed to work alongside surgeons — by developing a computer vision-based system that prevents collisions in real time. Using cameras and advanced image processing techniques, the system will monitor the operating room, detecting people and other obstacles. It […]