Scalable Algorithm

Scalable algorithms aim to efficiently solve computationally intensive problems, particularly those involving massive datasets or complex models, by minimizing resource consumption (time and memory) while maintaining accuracy. Current research focuses on developing scalable algorithms for diverse applications, including extreme classification (using approximate nearest neighbor search and novel negative sampling strategies), hypergraph learning (with methods like Convolutional Signal Propagation), active learning, and various clustering and optimization problems (employing techniques like local search, matrix sketching, and mean field theory). These advancements are crucial for tackling large-scale data analysis challenges across numerous fields, from machine learning and causal inference to recommendation systems and precision medicine.