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DTSTART;TZID=America/Los_Angeles:20261102T160000
DTEND;TZID=America/Los_Angeles:20261102T170000
DTSTAMP:20261008T203351Z
CREATED:20260921T170456Z
LAST-MODIFIED:20261008T203351Z
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SUMMARY:Statistics Seminar: Sliced Optimal Transport: From Acceleration to Bayesian Modeling
DESCRIPTION:Presenter: Enter the speaker’s name\, title\, and institution at the top of the event \nDescription: 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 and inference. In the first part of the talk\, I will briefly introduce optimal transport and sliced optimal transport (SOT). I will then present sliced-regularized OT\, which uses a cheap sliced plan as an informative prior for approximating the full plan\, yielding an efficient approximate OT solver. In the second part\, I will show how SOT enables new applications in statistical inference. In particular\, I will introduce distributional determinantal point processes\, which use SOT geometry to place repulsive priors on distributions\, giving a Bayesian model for clustering distributional data. Applications include image processing and the clustering of single-cell and epilepsy data. \nAbout the speaker: Dr. Khai Nguyen is an Assistant Professor in the Department of Statistics and Data Science at Texas A&M University. He received his Ph.D. in Statistics from the University of Texas at Austin and his B.Sc. in Computer Science from Hanoi University of Science and Technology. His research develops computational optimal transport\, particularly sliced methods that make large-scale transport tractable. He brings these tools to problems in machine learning and geometric data processing\, as well as to Bayesian approximate inference and nonparametric models for distributional data. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/am-seminar-sliced-optimal-transport-from-acceleration-to-bayesian-modeling/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20261102T160000
DTEND;TZID=America/Los_Angeles:20261102T170000
DTSTAMP:20261030T163324Z
CREATED:20261030T163324Z
LAST-MODIFIED:20261030T163324Z
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SUMMARY:AM Seminar: Local\, Matrix-Free Poisson Solve and its Projection Scheme for a High-Order Finite Volume MHD Method
DESCRIPTION:Presenter: Dongwook Lee\, Professor\, Applied Mathematics\, UC Santa Cruz \nDescription: 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 local method with performance comparable to a highly optimized FFT solver. Integrated into the MHD framework via a fifth-order Gaussian-process (GP) approximation\, SORh features a matrix-free\, single-stencil-pass-per-sweep structure. This allows straightforward extensions to mixed/non-periodic boundary conditions\, variable-coefficient elliptic operators\, and adaptive meshes. Benchmark tests\, including smooth circularly polarized Alfvén waves and shock-dominated problems\, validate that the new method matches highly optimized spectral FFT projection in accuracy\, stability\, and efficiency.
URL:https://events.ucsc.edu/event/am-seminar-local-matrix-free-poisson-solve-and-its-projection-scheme-for-a-high-order-finite-volume-mhd-method/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261109T160000
DTEND;TZID=America/Los_Angeles:20261109T170000
DTSTAMP:20261007T210443Z
CREATED:20261007T210443Z
LAST-MODIFIED:20261007T210443Z
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SUMMARY:AM Seminar: Enhanced Resolution Imaging in Strongly Scattering Media
DESCRIPTION:Presenter: Chrysoula Tsogka\, Professor\, Applied Mathematics\, UC Merced \nDescription: 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 than it is. This effect\, known as super-resolution\, is not available to conventional imaging. Since the medium through which the waves travelled is unknown\, conventional methods back-propagate the data in an approximation of the true background. In this talk I will show how super-resolution can be achieved in imaging when abundant array data are available. The key idea is to learn the Green’s functions of the unknown random medium directly from the data using sparse dictionary learning. The Green’s functions are recovered as unordered columns of the sensing matrix. To associate each column with its location in the imaging window\, we build a graph from cross-correlations of the recovered columns and apply multidimensional scaling to the resulting proxy distances. The learned Green’s functions are then used to form images by back-propagation or by $\ell_2$ and $\ell_1$ methods\, with resolution beyond the homogeneous medium limit. I will also discuss recent extensions: a new dictionary learning algorithm that starts from a random initialization\, imaging on unstructured grids\, and imaging through changing random media. Joint work with M. Moscoso\, A. Novikov\, G. Papanicolaou\, A. Christie\, M. Leibovich and J. Zheng. \nAbout the speaker: Chrysoula Tsogka is a Professor of Applied Mathematics at the University of California\, Merced\, where she also serves as Chair of the Applied Mathematics Graduate Group. She received her Ph.D. in Applied Mathematics from the University Paris IX Dauphine and was a Postdoctoral Fellow at Stanford University. Before joining UC Merced in 2019\, she was a tenured CNRS researcher at LMA in Marseille\, an Assistant Professor at the University of Chicago\, and a Professor at the University of Crete. Her research focuses on numerical methods for direct and inverse wave propagation problems and on imaging in complex media. Her recent work includes quantitative synthetic aperture radar (SAR) imaging for the detection and classification of buried landmines\, and data-driven methods\, such as dictionary learning\, for super-resolution imaging in strongly scattering media. She serves on the editorial boards of Inverse Problems\, the Journal of Computational Physics and the Journal of Mathematical Imaging and Vision.
URL:https://events.ucsc.edu/event/am-seminar-enhanced-resolution-imaging-in-strongly-scattering-media/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261116T160000
DTEND;TZID=America/Los_Angeles:20261116T170000
DTSTAMP:20261015T164013Z
CREATED:20261015T164013Z
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SUMMARY:Statistics Seminar: Evolutionary Analysis of Biobank-Scale Data to Study Population History\, Mutation Rates\, Natural Selection\, and the Genetics of Complex Traits
DESCRIPTION:Presenter: Joshua Schraiber\, Research Scientist\, University of Southern California and Cornell University \nDescription: 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\, and the action of strong natural selection. Moreover\, biobank data with phenotypes enables probing into the details of the genetic architecture of complex traits. However\, the large sample sizes and low frequency alleles found in such datasets often result in violations of common modeling assumptions in population and statistical genetics. To address this gap\, and gain the most possible information out of this data\, I will present biobank-scale evolutionary models. I will first show that ultra-rare mutations often violate the assumption that every mutation only occurred once in the history of the sample. By developing a model that explicitly accounts for recurrent mutation\, we are able to use ultra-rare mutations to learn fine-scale details about recent demography history and mutation rates. We then extended this model to account for natural selection\, and developed a Bayesian approach to share information between genes and learn the distribution of fitness effects at the level of individual genes. Finally\, I will show how rare variants violate the standard assumptions of models of complex trait architecture and demonstrate a modeling approach that can account for effect sizes among rare variants. This will show that the distribution of effect sizes among common variants in the human genome is approximately scale-free. Together\, these findings enhance our understanding of human biology and evolution in the biobank era. \nAbout the speaker: Joshua Schraiber is a Research Scientist working with Doc Edge at USC and Matt Pennell at Cornell University. His background is in evolutionary modeling and statistical methods development. Prior to his current role\, he was a Research Scientist at AncestryDNA and Illumina before returning to academia. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/statistics-seminar-evolutionary-analysis-of-biobank-scale-data-to-study-population-history-mutation-rates-natural-selection-and-the-genetics-of-complex-traits/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261116T160000
DTEND;TZID=America/Los_Angeles:20261116T170000
DTSTAMP:20261015T165727Z
CREATED:20261015T165630Z
LAST-MODIFIED:20261015T165727Z
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SUMMARY:AM Seminar: Heat Transport and Scalar Dissipation in Turbulent Flows: A Constructive Approach
DESCRIPTION:Presenter: Anuj Kumar\, Assistant Professor\, UC Davis \nDescription: 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. In this talk\, I will discuss a constructive approach to these questions through explicitly designed incompressible flows. We will first consider heat transport between walls held at different temperatures. Under a constraint on the power supply\, I will present a three dimensional branching construction that achieves the optimal heat transport scaling. By examining these designs carefully\, we will identify the physical mechanisms responsible for efficient heat transfer and discuss potential applications\, including the pressing problems of cooling in AI data centers. We will then turn to scalar turbulence. I will introduce a universal incompressible flow built from a carefully organized hierarchy of shear flows and explain how these constructions approach the sharp Obukhov–Corrsin regularity threshold for scalar dissipation. These constructions also exhibit eddy-diffusivity effect: advection mimics diffusion of order one even in the transport equation\, where molecular diffusion is entirely absent. Together\, these examples illustrate how the process of constructing flows that achieve sharp scaling laws can also reveal the mechanisms underlying enhanced transport and dissipation\, which are two key features of turbulence. \nAbout the speaker: Anuj Kumar is an Assistant Professor in the Department of Mathematics at the University of California Davis. His research lies at the intersection of fluid dynamics\, partial differential equations (PDEs) and applied mathematics\, combining computational\, mathematical analysis and theoretical approaches. Prior to joining UC Davis\, he was a Visiting Assistant Professor in the Department of Mathematics at the University of California Berkeley. He earned my Ph.D. in Applied Mathematics from the University of California Santa Cruz in 2023. \nThis seminar is hosted by Professor Pascale Garaud.
URL:https://events.ucsc.edu/event/am-seminar-heat-transport-and-scalar-dissipation-in-turbulent-flows-a-constructive-approach/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261123T160000
DTEND;TZID=America/Los_Angeles:20261123T170000
DTSTAMP:20261007T203353Z
CREATED:20261007T203353Z
LAST-MODIFIED:20261007T203353Z
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SUMMARY:Statistics Seminar: Randomization Posteriors and Generalized Bayesian Inference
DESCRIPTION:Presenter: Edric Tam\, Warren Alpert Fellow in AI and Computational Biology\, Department of Biomedical Data Science\, Stanford University \nDescription: 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 to the log-likelihood contributions of individual observations\, thereby averaging inference over random perturbations of the dataset. The resulting posterior updates are governed by a fundamental design choice: the stage at which the randomization is averaged out. Averaging at the log-likelihood level recovers the power posterior for a broad class of weight distributions\, while averaging at the posterior level\, under suitable choices of weights\, recovers the bagged posterior. Averaging at the likelihood level yields a new family of generalized Bayesian updates\, which we term contamination posteriors. These are indexed by the cumulant transform of the randomization law and require a reference density for well-posedness. For Bernoulli weights\, they recover classical Huber-type contamination models\, while Beta and other weight distributions produce new generalized posteriors. We characterize the theoretical properties of randomization posteriors and illustrate their empirical robustness through simulations and experiments. We also discuss connections with bootstrap methods\, coarsened posteriors\, and other generalized Bayesian updating procedures. \nAbout the speaker: Edric (Ed) Tam is a postdoctoral fellow at the Department of Biomedical Data Science in Stanford University. His research lies at the intersection of Bayesian statistics\, artificial intelligence\, and biomedical applications. His postdoctoral work has been supported by a Warren Alpert Fellowship and a Croucher Postdoctoral Fellowship. He earned his PhD in Statistical Science from Duke University\, where he worked with David Dunson\, and holds degrees in computer science and biomedical engineering from the University of Chicago\, Yale University\, and Johns Hopkins University. He has also held research and engineering roles at Google\, Meta\, Amazon\, and Apple AIML. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/statistics-seminar-randomization-posteriors-and-generalized-bayesian-inference/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261130T160000
DTEND;TZID=America/Los_Angeles:20261130T170000
DTSTAMP:20261015T171006Z
CREATED:20261015T171006Z
LAST-MODIFIED:20261015T171006Z
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SUMMARY:Statistics Seminar: Robustness\, Model Checking and Hierarchical Models
DESCRIPTION:Presenter: Rafael Cabral\, Member of Technical Staff\, Axiom Math \nDescription: 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 Gaussian latent process over-smooths\, impacting both inference and prediction. This talk presents a two-step workflow for hierarchical models\, in the spirit of George Box’s loop: model criticism\, to check whether the latent assumptions hold\, followed by robustification\, to fit a more flexible model when they do not. In the criticism step\, diagnostic statistics are derived rather than chosen ad hoc\, by targeting the departures from Gaussianity induced by a more flexible alternative model. This connects Bayesian predictive checking with robustness analysis and yields a general recipe for assessing latent assumptions\, and their impact on the results. In the robustification step\, latent non-Gaussian models are fitted using variational Bayes and Laplace approximations. The resulting algorithm iteratively refits LGMs that downweight extreme events in the latent process\, giving robust inferences at a fraction of the cost of MCMC. Both steps are implemented in Stan and R-INLA and illustrated with longitudinal\, areal and geostatistical applications. \nAbout the speaker: Rafael Cabral is a Member of Technical Staff at Axiom Math in Palo Alto\, where he develops AI systems for mathematics and verified reasoning. He received his PhD in Statistics from KAUST in 2023\, advised by Håvard Rue and David Bolin\, where he worked on model criticism and robust inference for latent Gaussian models. He was subsequently a Research Fellow at the National University of Singapore and a Staff Engineer at Huawei\, working on AI safety and reliability. His research interests span Bayesian modeling\, robustness\, and the application of language models to mathematics and formal reasoning. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/statistics-seminar-robustness-model-checking-and-hierarchical-models/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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