Gaussian Processes Poster Session

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Presenter: Matthew Bonas (University of Notre Dame)
Collaborator(s): Stefano Castruccio
Title: Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks
Presenter: Jian Cao (Texas A&M University, College Station)
Collaborator(s): Joseph Guinness Marc G. Genton Matthias Katzfuss
Title: Vecchia Gaussian-Process Regression and Variable Selection
Presenter: Moses Chan (Northwestern University)
Collaborator(s): Matthew Plumlee
Title: Applying Variational Inference on High-Dimensional Gaussian Process with Inducing Points
Presenter: Haoyuan Chen (Texas A&M University, College Station)
Collaborator(s): Dr. Liang Ding, Dr. Rui Tuo
Title: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Presenter: Debangan Dey (NIH – National Institutes of Health)
Collaborator(s): Andrew Finley, Abhirup Datta, Sudipto Banerjee
Title: Graphical Nearest Neighbor Gaussian Process Models for Big Spatial Data
Presenter: Youssef Fahmy (Cornell University)
Collaborator(s): Joe Guinness
Title: Multivariate Matérn Vecchia Approximations and Optimization for Multivariate Matérn Models
Presenter: Haoxiang Feng (Michigan State University)
Collaborator(s): Nian Liu
Title: Computationally Efficient Estimators for Ornstein-Uhlenbeck Processes on Fixed Domains
Presenter: Christopher Geoga (Rutgers University)
Collaborator(s): Michael L. Stein
Title: A Scalable Method to Exploit Screening in Gaussian Process Models with Noise
Presenter: Whitney Huang (Clemson University)
Collaborator(s): Yu-Min Chung; Yu-Bo Wang; Jeff Mandel; Hau-Tieng Wu
Title: Predicting high frequency biomedical signal using synchrosqueezing transform and locally stationary Gaussian process regression
Presenter: Felix Jimenez (Texas A&M University, College Station)
Collaborator(s): Matthias Katzfuss
Title: Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Presenter: Myeongjong Kang (Texas A&M University, College Station)
Collaborator(s): Matthias Katzfuss
Title: Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference
Presenter: Charles Kulick (University of California, Santa Barbara (UCSB))
Collaborator(s): Sui Tang, Jinchao Feng, Mengyang Gu
Title: Scalable Model Selection of Particle Swarming Models with Gaussian Processes
Presenter: Kaiyu Li (University College London)
Collaborator(s): Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol
Title: Multilevel Bayesian Quadrature
Presenter: Xubo Liu (University of California, Santa Barbara (UCSB))
Collaborator(s): Mengyang Gu
Title: Scalable marginalization of latent variables for correlated data
Presenter: Mary Salvana (University of Houston)
Collaborator(s): Mikyoung Jun
Title: Global 3D Bivariate Nonstationary Spatial Modeling of Argo Ocean Temperature and Salinity Profiles
Presenter: Annie Sauer (Virginia Polytechnic Institute & State University (Virginia Tech))
Collaborator(s): Robert B. Gramacy and David Higdon
Title: Active Learning for Deep Gaussian Process Surrogates
Presenter: Julia Walchessen (Carnegie-Mellon University)
Collaborator(s): Amanda Lenzi and Mikael Kuusela
Title: Learning Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihood Functions
Presenter: Stephen A Walsh (Virginia Polytechnic Institute & State University (Virginia Tech))
Collaborator(s): Dave Higdon, Annie Sauer, Marco A. R. Ferreira, Stephanie Zick
Title: A Deep Gaussian Process Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts
Presenter: Lu Zhang (University of Southern California (USC) Medical School)
Title: Bayesian Predictive Stacking Under Spatial Process Settings
Presenter: Yingchao Zhou (Iowa State University)
Title: Can spatial data benefit from conformal prediction?