Department of Software Engineering
The Multimodal Interaction Group investigates human-computer interaction across diverse contexts, including workplaces, homes, and public spaces, with a focus on current and emerging technologies. Our research delves into the theoretical and technical foundations of multimodal interaction, encompassing concepts, models, and the development of innovative software tools and interactive systems. We are particularly interested in exploring how humans interact with digital devices in various settings and how these interactions are influenced by psychological and software factors. Our research encompasses a wide range of topics, including but not limited to context-aware computing, perceptual Interfaces, collaborative and learning environments, and interactive data exploration and presentation.
The Software Testing and Reliability Group is a dedicated research group focused on advancing the science and practice of software testing and ensuring the reliability of software systems. It conducts in-depth research in various critical areas, including software testing methodologies, quality assurance processes, fault detection, and software reliability analysis. The group explores both traditional and emerging testing techniques, such as automated testing, model- based testing, and fault injection, to enhance the accuracy and efficiency of software testing. The group also investigates advanced approaches to improve software robustness, fault tolerance, and performance across different software applications and industries. By developing innovative tools, frameworks, and best practices, the center aims to contribute to the creation of more dependable and high-quality software systems, addressing the growing complexity and challenges in modern software development.
The Cognitive AI and Pattern Intelligence Group focuses on developing advanced artificial intelligence systems capable of understanding, analysing, and interpreting complex patterns across diverse data types, including visual, textual, auditory, and multimodal data. By integrating cognitive science principles with state-of-the-art AI and machine learning techniques, our research aims to create intelligent systems that can learn, reason, and adapt to dynamic environments. Key research areas include pattern recognition, predictive analytics, natural language understanding, anomaly detection, and decision-making systems. Our objective is to bridge the gap between human cognitive capabilities and artificial intelligence, driving innovation across fields such as healthcare, robotics, finance, and smart cities while fostering collaborative research and real-world impact.