ARTIFICIAL INTELLIGENCE & MACHINE LEARNING LABS & FACILITIES
Adaptive Neuromorphics, Intelligence, Memory and Unified Systems (ANIMUS) Lab
The Adaptive Neuromorphics, Intelligence, Memory and Unified Systems (ANIMUS) Lab seeks to understand the nature of cognition through the use of computational models and artificial intelligence. Work in the lab focuses on developing new kinds of artificial neural networks, neural models and cognitive architectures inspired by cutting-edge developments in artificial intelligence and cognitive science.
In particular, researchers are interested in examining the ways current AI falls short of emulating human behaviour and cognition and how the current gulf between AI and natural minds can potentially be bridged.
Artificial Intelligence and Machine Learning Lab
The Artificial Intelligence and Machine Learning (AIML) research lab concentrates on a serious of AI and ML problems with intentions of automating the learning process, and reducing the dependence of learning systems on human guidance. Lab members apply research across various applications, including computer vision, natural language processing and medical data analysis.
Biomedical Informatics Collaboratory
The Carleton University Biomedical Informatics Collaboratory (cuBIC) aims to tackle problems in biomedical informatics and bioinformatics. To conduct their research, cuBIC applies traditional machine learning, deep learning, signal processing and data science.
Intelligent Machines Lab
Researchers within the Intelligent Machines Lab (iML) perform fundamental and applied research in machine learning, deep learning and related areas in computer vision and natural language processing. More specifically, researchers are interested in interpretable machine learning, deep neural networks and transfer learning.
The goal is to create ML models with explainability in mind, or to develop methods that can decipher existing black-box ML models. Researchers are also interested in how ML models can learn to perform new tasks with limited amount of labelled data; a capability that people are very good at.
Interactive Media Group
The Carleton University Interactive Media Group (iMG) follows a multidisciplinary approach and utilizes a number of disciplines, including: human-computer interaction, computer games and animation, artificial intelligence, visualization, digital art, and various technologies for communication, collaboration, health, and education.
Multi-Domain Laboratory
Focusing on the dynamics and mechatronics, the mission of the research program is to develop enabling technologies and simulations, which compensate for the relative motion on any axis. To accomplish the program’s mission, advances must be made in the mechanical, electrical, computer, fluid, control and robotic research fields including machine learning and artificial intelligence. This work can be applied to aerial, terrestrial and maritime robotic applications, hence the name: Multi-Domain Laboratory.
Network Management and Artificial Intelligence Lab
The Network Management and Artificial Intelligence (NMAI) Laboratory is focused on the general domains of artificial intelligence and networking. Some of their ongoing projects include autonomous vehicles, software agent imitation and peer-to-peer networking.
Next Generation Networks Lab
At the forefront of cutting-edge technology, the Next Generation Networks Lab is supervised by Carleton Prof. Ashraf Matrawy. The lab focuses on a number of research areas, including: secure transport protocols for IoT, 5G security, 5G threat modelling, machine learning applied to network security, and IoT security.
Real-time and Distributed Systems (RADS) Research Centre
The Real Time and Distributed Systems (RADS) research centre is internationally known for the calibre of its investigation. At RADS, the mission is to perform both fundamental and applied research in a variety of ICT disciplines, including: system performance, software engineering, modelling and simulation, system optimization, resource management, system security, artificial intelligence and machine learning, as well as mobile and wireless systems.
Additionally, RADS is focused on interdisciplinary research in the fields of civil/mechanical engineering and cognitive science.
Reasoning for Interactive Systems and Experiences
The Reasoning for Interactive Systems and Experiences (RISE) multidisciplinary research group is located at Carleton University and Reykjavik University (Iceland). Mainly, this group focuses on the intersection of artificial intelligence and human-computer interaction. Ultimately, the objective is to enable more effective interactions between people and AI systems.
Robotics, Navigation and Control Systems Laboratory
At RNCSL, researchers develop state-of-the-art solutions for robotic autonomy applications. They explore the fundamental research problem, study system dynamics and develop advanced solutions applicable for: situational awareness, vision-aided and perception systems, control systems, filtering and estimation, perception and mapping, trajectory control and motion planning.
Researchers aspire to discover new ways to improve the performance of dynamical systems, in particular, smart systems which include semi-automated and fully autonomous systems, such as unmanned aerial vehicles (UAVs/drones), autonomous underwater vehicles (AUVs), mobile robots, satellites and other robotics applications.
Signal Processing and Machine Learning Lab
The Signal Processing and Machine Learning lab focuses on developing algorithms and techniques to extract meaningful information from various types of signals, such as audio, video and sensor data from multiple sources and modalities. The research aims to improve signal processing methods, including noise reduction, feature extraction, signal separation, audio-visual scene analysis and source localization.
By leveraging machine learning techniques, the lab aims to improve the accuracy and efficiency of signal separation algorithms through machine learning techniques, enabling better understanding and extraction of information from mixed signals into their individual components. The research conducted in this lab has the potential to revolutionize applications ranging from speech recognition and audio enhancement to multimedia analysis, human-computer interaction and biomedical signal decomposition.
Silicon Micro/NanoPhotonics Laboratory
Led by Prof. Winnie Ye, the Silicon Micro/NanoPhotonics Laboratory is a graduate research laboratory within the Department of Electronics. The laboratory engages in cutting-edge research in the area of silicon photonics. Specific areas of applications include data communication and telecommunication as well as biophotonics and renewable energy. As a new extension of the work in the field of optical communication and data communication, machine learning and artificial neural networks are explored for accelerated photonics design. The laboratory works in close collaboration with industries and governmental organizations.
Software Quality Engineering Lab
The Software Quality Engineering Laboratory (SQUALL) is an industry-oriented, software engineering research lab. At SQUALL, researchers adopt a rigorous, problem-driven approach to every problem, and views software development as an engineering discipline.
Many of their projects revolve around Unified Modeling language (UML). One of their research interests in artificial intelligence and its application to evolutionary computing to software engineering.
Spacecraft Robotics and Control Laboratory
Housed within the Department of Mechanical and Aerospace Engineering, the Spacecraft Robotics and Control Laboratory tackles cutting-edge research in the general areas of machine learning, control, and robotics.
