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Department of Applied Mathematics and Theoretical Physics, Department of Mathematical Sciences, University of Cambridge, University of Bath
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Identifying piecewise-smooth dynamical systems could revolutionize our understanding of complex systems in climate dynamics and mechanics.
Directly reconstructing protein backbones from cryo-EM data without intermediate electrostatic maps reveals new potential for capturing complex conformational changes.
Achieving robust neural network performance on non-Euclidean spaces could redefine stability benchmarks in machine learning applications.
Achieve ~20% gains on difficult 3D medical image segmentation by explicitly removing noisy activations in U-Net skip connections with a novel proximal-sparse attention mechanism.
Lipschitz-constrained Transformers, built from gradient flows, can provably approximate any Lipschitz-continuous function, offering a path to more robust and stable architectures.
