Haoming Shen Source Confirmed

OverviewAI-generated summary

Haoming Shen's research focuses on the theoretical and algorithmic development of optimization techniques, particularly in the realm of stochastic optimization and chance-constrained programming. His work addresses the complexities of decision-making under uncertainty, exploring methods to find robust solutions when outcomes are subject to random variations and ambiguity. Shen has published research on convex chance-constrained programs incorporating Wasserstein ambiguity, indicating a focus on quantifying and managing distributional uncertainty. He also investigates sequential quadratic optimization as a method for solving expectation equality constrained stochastic optimization problems, and has published on the theory and algorithms of robust chance constraints. His scholarly contributions include four publications with a total of 12 citations and an h-index of 2.

Metrics

• h-index: 2
• Publications: 4
• Citations: 12