Camilo A. Duarte
Papers
1
Total Citations
3
H-Index
1
About
Camilo A. Duarte’s research lies at the intersection of stochastic control, risk-sensitive optimization, and dynamical systems, with a particular focus on developing algorithms that can operate reliably under uncertainty. His most notable contribution is a general framework for optimizing the Conditional Value-at-Risk (CVaR) in dynamical systems, introduced in his 2020 paper “Adaptive CVaR Optimization for Dynamical Systems with Path Space Stochastic Search.” This work provides a principled method for handling multiple sources of uncertainty—including initial conditions, stochastic dynamics, and uncertain model parameters—by leveraging stochastic search over path space. The approach enables risk-aware decision-making in complex, high-dimensional systems where traditional methods fall short. While his citation count is still growing, Duarte’s work is gaining traction among researchers in robotics, autonomous systems, and control theory who seek robust, risk-sensitive solutions. His framework has been benchmarked against existing algorithms, demonstrating clear advantages in scenarios requiring safety and reliability under uncertainty. Duarte’s contributions are particularly relevant for applications in autonomous navigation, finance, and any domain where worst-case outcomes must be systematically mitigated.
Research Focus
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Top Papers
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