Kelvin Jordan

Keller Jordan is a researcher at OpenAI, which he joined in December 2024. He is best known for creating the Muon optimizer, a neural network training algorithm that uses Newton-Schulz iterations to update hidden-layer parameters, demonstrating substantially faster convergence than standard optimizers like Adam. Jordan holds a double bachelor's degree in mathematics and computer science from UC San Diego and previously worked as a machine learning engineer at Hive and as a visiting researcher at the Vienna Complex Systems Center; OpenAI recruited him based solely on a blog post he published introducing Muon.