Liliaokeawawa Cothren
Papers
1
Total Citations
11
H-Index
1
About
Liliaokeawawa Cothren is a researcher advancing the frontier of data-driven control for complex dynamical systems, with a focus on integrating deep learning perception into real-time optimization. Her most-cited work, "Online Optimization of Dynamical Systems With Deep Learning Perception" (2022, 11 citations), tackles a critical challenge: controlling systems where the state is unmeasurable and performance metrics are partially unknown. She develops data-driven controllers that regulate dynamical systems to desired outcomes by leveraging perception-based inputs, bridging the gap between high-dimensional sensor data and low-level control. This contribution is particularly impactful for autonomous systems operating in uncertain environments, such as robotics and autonomous vehicles, where traditional model-based approaches fail. Cothren’s research sits at the intersection of control theory, machine learning, and optimization, offering practical solutions for real-time adaptation. Her work is notable for its theoretical rigor and applied relevance, providing a framework that enables systems to learn and optimize online without explicit models. As a rising voice in this interdisciplinary field, Cothren’s contributions are shaping how engineers design resilient, perception-driven controllers for next-generation autonomous technologies.
Research Focus
Key Achievements
Top Papers
- 1Online Optimization of Dynamical Systems With Deep Learning Perception11 citations · 2022