Abigail G. Keller
Papers
1
Total Citations
3
H-Index
1
About
Abigail G. Keller is a rising scholar in the field of control theory and decision-making under uncertainty, with a focus on the intersection of computational efficiency and real-world applicability. Her most-cited work, "Pretty Darn Good Control: When are Approximate Solutions Better than Approximate Models" (2023), challenges conventional wisdom by demonstrating that, in many practical scenarios, using approximate control solutions can outperform efforts to build highly accurate models—a counterintuitive insight with profound implications for robotics, autonomous systems, and resource-constrained environments. While still early in her career, Keller’s research has already garnered attention for its pragmatic approach to bridging theoretical rigor and engineering practice. Her work is particularly notable for its clarity in framing trade-offs between model fidelity and solution optimality, offering a new lens for designing efficient algorithms. As she continues to develop her portfolio, Keller’s contributions promise to shape how engineers and researchers think about control in messy, real-world systems where perfect models are unattainable.
Research Focus
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Top Papers
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