Machine learning is a field of study that gives computers the ability to learn
without being explicitly programmed. While traditional algorithms and methods of machine learning are being used everywhere it's good to use new approaches and ways of thinking about the same old problems. One way is using the new Graph Learning methods. Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. Most of relational structures in real world do not satisfy the simplicity required to be analyzed by neural networks. When neural networks fail, graph learning prevails!
Graphs are a mathematical representation of real physical and abstract networks. There are algorithms that draw graphs automatically to make networks more accessible to humans. But what is a better way for analyzing graphs than drawing them! But seriously, Graph drawing is an area of mathematics and computer science combining methods from geometric graph theory and information visualization to derive two-dimensional depictions of graphs arising from applications such as social network analysis, cartography, linguistics, and bioinformatics.
Many computer scientists works on the mathematical foundations of computing. Current research areas of our lab include algorithm, data structures, and computational geometry. Computational geometry is the study of efficient algorithms to solve geometric problems. The methodologies of computational geometry allow one to design and analyze algorithms for the efficient solution of numerous geometric problems that arise in application areas such as robotics (e.g., motion planning among obstacles), geographic information systems (GIS), (e.g. combining several layers of information in one map), service location problems (e.g., cellular-antenna placement), Computer Aided Design and Manufacturing (CAD/CAM) systems, and integrated circuit design/verification systems.