Research
Our current research directions and ongoing work in artificial intelligence and cognitive systems.
Current Research Areas
Machine Learning
Exploration of learning algorithms, optimization techniques, and theoretical foundations that enable systems to improve through experience.
Deep Learning
Investigation of neural network architectures, training methodologies, and representational learning that capture complex patterns in data.
Reinforcement Learning
Research into agent-based learning systems that make decisions through interaction with environments, with applications to reasoning and planning.
Reasoning Systems
Development of computational frameworks that enable systematic reasoning, logical inference, and problem-solving capabilities.
Cognitive Architectures
Exploration of system designs that model aspects of human cognition, including memory, attention, and decision-making processes.
Research Output
Research is ongoing. Publications and results will be shared as work progresses and reaches appropriate stages of completion.