Alfredo J. Duarte
Papers
1
Total Citations
7
H-Index
1
About
Alfredo J. Duarte is a rising scholar in stochastic optimal control and risk-aware decision-making, with a focus on bridging rigorous mathematical theory and practical computational methods. His work centers on chance-constrained stochastic optimal control, where he develops novel frameworks to handle uncertainty and risk constraints in continuous-time, continuous-space systems. In his most-cited paper (2022, 7 citations), Duarte introduces a path integral and finite difference approach to solve a Hamilton-Jacobi-Bellman (HJB) partial differential equation, converting a risk-constrained problem into a risk-minimization formulation via Lagrangian relaxation. This contribution offers a tractable pathway for addressing safety-critical control problems in robotics, finance, and autonomous systems. While early in his career, Duarte’s work demonstrates a clear ability to merge theoretical depth with algorithmic innovation, laying groundwork for future advances in risk-aware control under uncertainty. His research is particularly valuable for students and engineers seeking principled methods to handle stochastic constraints in real-world applications.
Research Focus
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Top Papers
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