Moses Charikar

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About

Moses Charikar is a towering figure in theoretical computer science, renowned for his profound contributions to approximation algorithms, metric embeddings, and high-dimensional data analysis. His work has fundamentally shaped how we understand and solve problems in clustering, dimensionality reduction, and online computation. Among his most celebrated achievements is the development of the "Locality-Sensitive Hashing" (LSH) framework, a breakthrough that revolutionized nearest-neighbor search in high-dimensional spaces and is now a cornerstone of modern machine learning and data mining. Charikar’s research on the "k-median" and "k-center" clustering problems has yielded optimal or near-optimal approximation algorithms, many of which are taught as canonical examples in graduate courses. His early work on the "dynamic servers problem" (1998, 16 citations) introduced a novel generalization of the classic k-server problem, allowing algorithms to flexibly manage server resources while paying rental costs—a concept that has influenced subsequent research in online algorithms. With tens of thousands of citations across his body of work, Charikar’s impact is immense; he is a recipient of the prestigious ACM Doctoral Dissertation Award and a fellow of the ACM, and his algorithms are widely implemented in systems from search engines to bioinformatics.