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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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The geometry and metric structure of data
How faithfully can we represent complex data with simple geometric structure — trees, hyperbolic spaces, and metric spaces — and what goes wrong when we cannot?
Machine learning for the physical and life sciences
Using machine learning to accelerate scientific discovery — from predicting metallic glasses to selecting markers in single-cell genomics.
Privacy, sparse recovery, and inverse problems
Recovering signals from few measurements while respecting constraints such as differential privacy, and solving nonlinear inverse problems.
Foundations of sparse approximation
The mathematical foundations underpinning my applied work: sparse approximation, sublinear-time and sparse Fourier algorithms, and compressive sensing.
TEMPEST: closure models for turbulence across scales
An NSF Science and Technology Center on real-world turbulence. My group builds the evaluation methods and computational infrastructure to compare closure models, whether analytic, learned, or hybrid, fairly and reproducibly.