Isabel Haasler
Papers
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Total Citations
2
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About
Isabel Haasler is a rising researcher at the forefront of mean field control and optimal transport theory, with a focus on multi-agent systems and structured optimization. Her key research areas include mean field games, entropy-regularized optimal transport, and tensor-based computational methods for large-scale dynamical systems. In her most-cited work, "Mean Field Type Control With Species Dependent Dynamics via Structured Tensor Optimization" (2023), Haasler introduced a novel formulation of mean field control problems involving multiple species with distinct dynamics. By recasting the discretized problem as an entropy-regularized multimarginal optimal transport problem with a decomposable structured tensor cost, she developed a new algorithmic framework that efficiently handles complex, heterogeneous agent interactions. This contribution bridges the gap between theoretical mean field models and practical computation, offering a scalable approach to problems in economics, robotics, and population dynamics. With her work already garnering attention in the optimization and control communities, Haasler is establishing herself as a key innovator in the mathematical foundations of multi-agent decision-making. Her research promises to advance both the theory and application of structured tensor methods in optimal transport and control.
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