Dear Colleagues,
The ASA Section on Statistics in Defense and National Security is pleased to announce an upcoming webinar by Dr. Amanda Muyskens of Lawrence Livermore National Laboratory on September 21, 2022.
For more information about the SDNS webinar series, please visit the ASA SDNS website or reach out to Elise (SDNS.AmStat@gmail.com) if you have any questions!
Speaker: Amanda Muyskens, Ph.D., Computational Engineering Division at Lawrence Livermore National Laboratory
Title: MuyGPs: Scalable Gaussian Process Model Estimation with Uncertainty Quantification
Date: Wednesday, September 21st
Time: 2:00 – 3:30 PM Eastern / 11:00 AM – 12:30 PM Pacific
Registration (Zoom, free): SDNS Webinar Registration
Abstract: The utilization of large and complex data by machine learning in support of decision-making is of increasing importance in many scientific and national security domains. However, the need for uncertainty estimates or similar confidence indicators inhibits the integration of many popular machine learning pipelines, such as those that rely upon deep learning. In contrast Gaussian Process (GP) models are popular for their principled uncertainty quantification but require quadratic memory to store the covariance matrix and cubic computation to perform inference or evaluate the likelihood function. We present MuyGPs, a novel computationally efficient GP hyperparameter estimation method for large data that has recently been released for open-source use in the python package MuyGPyS (https://github.com/LLNL/MuyGPyS). MuyGPs builds upon prior methods that take advantage of nearest neighbors structure for sparsification and uses leave-one-out cross-validation to optimize covariance (kernel) hyperparameters without realizing the expensive multivariate normal likelihood. We describe our model and approximate methods and compare our implementations against the state-of-the-art competitors in approximate GP regression in space-based applications. Finally, we discuss recent and future advances in MuyGPs including HPC integration and non-stationary models.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. IM release number: LLNL-ABS-832607.
About the Speaker: Dr. Amanda Muyskens is a statistician in the Applied Statistic Group (ASG) within the Computational Engineering Division (CED) at Lawrence Livermore National Laboratory (LLNL). She is currently leads the MuyGPs exploratory research project that has developed a novel method for scalable non-stationary Gaussian processes for high performance computing (HPC). Her data science expertise includes surrogate modeling, Gaussian process models, computationally efficient machine learning, uncertainty quantification, and statistical consulting. Dr. Muyskens received bachelor’s degrees in both mathematics and music performance from the University of Cincinnati in 2013 and a MS and PhD from NC State University in statistics in 2015 and 2019 respectively.
------------------------------
Elise Roberts
Publications Officer
Statistics in Defense and National Security
SDNS.AmStat@gmail.com------------------------------