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DTSTART;TZID=America/Los_Angeles:20261026T160000
DTEND;TZID=America/Los_Angeles:20261026T170000
DTSTAMP:20261019T173811Z
CREATED:20261019T173616Z
LAST-MODIFIED:20261019T173811Z
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SUMMARY:Statistics Seminar:  Calibration Methods for Generative Models with Application to Protein Structure Modeling
DESCRIPTION:Presenter: Brian Trippe\, Assistant Professor\, Stanford University\, Department of Statistics \nDescription: Generative models frequently suffer miscalibration\, where the statistics of their generations deviate from desired values. Protein structure diffusion models\, for example\, produce highly realistic samples but often underestimate the probabilities of important modes. These deviations are not addressed by existing fine-tuning methods for image and text generation. We frame calibration of generative models as a constrained optimization problem that we seek to solve by fine-tuning. Because the natural objective is intractable\, we introduce two surrogate objectives for which we can compute low-variance gradient estimates amenable to stochastic optimization. The resulting procedures reduce the majority of calibration error across hundreds of simultaneous constraints and models with up to nine billion parameters. Lastly\, we describe an application for assimilating thermodynamic measurements to calibrate a generative model of protein structure Boltzmann ensembles. \nAbout the speaker: Brian Trippe is an Assistant Professor in the Department of Statistics at Stanford. Previously he was a visiting researcher at the Institute for Protein Design at the University of Washington where he worked with David Baker\, and a postdoctoral fellow at Columbia University. He completed his Ph.D. in Computational and Systems Biology at MIT in 2022. His recent research has developed statistical machine learning methods to address challenges in biotechnology and medicine\, with a focus on generative modeling and inference algorithms for protein engineering. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-calibration-methods-for-generative-models-with-application-to-protein-structure-modeling/
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: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://live-events-ucsc.pantheonsite.io/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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261116T160000
DTEND;TZID=America/Los_Angeles:20261116T170000
DTSTAMP:20261015T164013Z
CREATED:20261015T164013Z
LAST-MODIFIED: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://live-events-ucsc.pantheonsite.io/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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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://live-events-ucsc.pantheonsite.io/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://live-events-ucsc.pantheonsite.io/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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