Presenter: Enter the speaker’s name, title, and institution at the top of the event Description: Optimal transport (OT) provides a principled way to match, compare, and interpolate distributions, but its computational cost limits its use at realistic scales. I will present our recent work on reducing that cost and on using OT for statistical modeling […]
Presenter: Dongwook Lee, Professor, Applied Mathematics, UC Santa Cruz Description: We present a fifth-order accurate multidimensional finite volume MHD scheme using a divergence-cleaning projection method to preserve the magnetic field’s solenoidal constraint. The projection step employs a new hyperbolic-variant successive over-relaxation (SORh) iteration to solve the Poisson equation. Unlike conventional global FFT approaches, SORh is a […]
Presenter: Chrysoula Tsogka, Professor, Applied Mathematics, UC Merced Description: In a strongly scattering medium, waves undergo multiple scattering that is usually seen as an obstacle to imaging. Time-reversal experiments show the opposite: waves re-emitted into the medium refocus on their source more sharply than in a homogeneous medium, because scattering makes the array appear larger […]
Presenter: Joshua Schraiber, Research Scientist, University of Southern California and Cornell University Description: The advent of biobank data has presented an unprecedented opportunity to understand the evolutionary history of the human species. By collecting genotype data from hundreds of thousands of individuals, we can look closely at recent evolutionary history, including recent demography, mutation rates, […]
Presenter: Anuj Kumar, Assistant Professor, UC Davis Description: Turbulent flows can transport heat and momentum much more efficiently than pure molecular diffusion alone. What flow structures enable this efficient transport and how can they sustain dissipation when diffusivity or viscosity is small? These questions are fundamental to understanding turbulence and have important applications in engineering. […]
Presenter: Edric Tam, Warren Alpert Fellow in AI and Computational Biology, Department of Biomedical Data Science, Stanford University Description: A longstanding question in Bayesian analysis concerns the role of randomization. We introduce randomization posteriors, a class of generalized Bayesian updates obtained by averaging over an auxiliary randomization law. A particularly relevant instance assigns random weights […]
Presenter: Rafael Cabral, Member of Technical Staff, Axiom Math Description: Latent Gaussian models (LGMs) are among the most widely used classes of statistical models, with applications ranging from biostatistics to spatial modeling. Their central assumption, that the latent variables are Gaussian, is nevertheless rarely checked. Many datasets contain sudden jumps or local spikes that a […]