Shiqian Ma

Rice University, Tianjin Research Institute of Electric Science (China)

Papers

Total Citations

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About

Shiqian Ma is a leading researcher in the fields of optimization, machine learning, and power systems, with a particular focus on developing novel algorithms for complex, high-dimensional problems. His most impactful work introduces groundbreaking methods for stochastic zeroth-order optimization on Riemannian manifolds, a challenging area where only noisy function evaluations are available. This contribution, detailed in his 2022 paper with 20 citations, provides efficient estimators for Riemannian gradients, enabling optimization over curved spaces without direct gradient access—a critical advance for modern machine learning and signal processing. Ma also bridges optimization with practical engineering, as seen in his 2018 work on AI-driven smart operating robots for power systems. This paper, with 6 citations, proposes a novel design framework that integrates artificial intelligence into power system control centers, addressing real-world demands for automation and intelligence in energy infrastructure. Through his dual focus on theoretical optimization and applied AI, Ma has established himself as a versatile scholar whose work influences both algorithmic foundations and their deployment in critical systems.