Mengxi Wu 武梦溪

Research

My research focuses on the theoretical foundations for improving efficiency and reducing the computational cost of training large language models (LLMs). Specifically, I am interested in developing new optimizers or improving existing ones (e.g., AdamW, Sophia). I also work on the theory of hyperparameter optimization by analyzing the mathematical relationships between hyperparameters (e.g., weight decay), model architectures, and dataset characteristics across different scales. Previously, my work included transfer learning with theoretical guarantees, adversarial machine learning for 3D point cloud processing, and streaming algorithms for time-varying volume data.