Behnoosh Parsa
Papers
1
Total Citations
5
H-Index
1
About
Behnoosh Parsa is a researcher at the forefront of computational modeling and control of complex dynamical systems. Her primary research areas include stochastic dynamics approximation, Bayesian machine learning, and model-based control for autonomous systems. Parsa’s major contribution lies in developing a hierarchical Bayesian linear regression model with local features, which addresses a critical challenge in model-based control: efficiently and accurately predicting state transitions in stochastic dynamical systems. This work, published in 2018 and garnering 5 citations, provides a novel representational framework that improves the quality of state predictions while reducing computational complexity—a significant advancement for autonomous systems operating under uncertainty. By integrating local feature selection with hierarchical Bayesian inference, Parsa’s approach enables more robust and adaptive control strategies. Her research is particularly notable for bridging the gap between probabilistic modeling and practical control engineering, offering scalable solutions for real-world autonomous platforms. Parsa’s work continues to influence the development of data-driven methods in robotics and control, making her a rising voice in the intersection of machine learning and dynamical systems theory.
Research Focus
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Top Papers
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