Research Areas

This fundamental area of research is concerned with the design, analysis, and application of efficient computational methods and data organization. Key areas of investigation within the department include computational geometry, focusing on algorithms for geometric problems, and combinatorial optimization, which seeks optimal solutions from a finite set of possibilities. This research has direct applications in solving complex logistical challenges such as facility location problems and developing effective heuristic algorithms for various optimization tasks.

This domain focuses on creating intelligent systems capable of learning from data and making predictions or decisions. Departmental research explores a wide spectrum of topics, including data mining for discovering patterns in large datasets and various pattern recognition techniques. A significant thrust is given to the application of soft and evolutionary computing for tackling complex optimization problems. Furthermore, current research investigates deep learning models, such as autoencoders, for advanced applications in image characterization and analysis.

This field involves the computational analysis and interpretation of digital images to automate tasks that the human visual system can do. Research activities in the department are diverse, spanning applications from medical image analysis to remote sensing and GIS-based systems. Core research problems addressed include image segmentation, feature extraction, and the application of pattern recognition techniques for classification and interpretation of visual data.