Large Scale

Large-scale data processing and analysis are central to addressing numerous scientific and engineering challenges, focusing on efficient handling of massive datasets and complex systems. Current research emphasizes developing novel algorithms and model architectures, such as graph neural networks, deep learning models, and physics-guided machine learning, to improve efficiency, accuracy, and scalability in diverse applications. These advancements are crucial for tackling problems ranging from traffic optimization and robot navigation to astronomical surveys and the development of more energy-efficient AI systems. The resulting insights and tools have significant implications across various fields, enabling more effective data-driven decision-making and scientific discovery.