Research Overview

My research lies at the interface of optimization, statistical learning, and machine learning, with a focus on the theory and design of efficient algorithms for large-scale problems. I am particularly interested in understanding how computational, memory, and communication constraints shape optimization and statistical performance. My work covers stochastic and non-convex optimization, distributed and federated learning, communication compression, adaptive and second-order methods, and model compression through sparsity, quantization, and low-rank structure. A recurring theme is to develop algorithms with rigorous convergence and complexity guarantees while accounting for constraints that arise in modern large-scale learning systems. More broadly, I aim to connect mathematical foundations in optimization with practical questions of scalability, robustness, and resource efficiency.