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09/05/2023
Ab initio uncertainty quantification in scattering analysis of microscopy
Estimating parameters from data is a fundamental problem in physics, cus...
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05/15/2023
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
Data-driven modeling is useful for reconstructing nonlinear dynamical sy...
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05/06/2023
A Nonparametric Mixed-Effects Mixture Model for Patterns of Clinical Measurements Associated with COVID-19
Some patients with COVID-19 show changes in signs and symptoms such as t...
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03/07/2023
Non-Gaussian and anisotropic fluctuations mediate the progression of global cellular order: a data-driven study
The dynamics of cellular pattern formation are crucial for understanding...
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08/13/2022
Reliable emulation of complex functionals by active learning with error control
Statistical emulator is a surrogate model of complex physical models to ...
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03/16/2022
Scalable marginalization of latent variables for correlated data
Marginalization of latent variables or nuisance parameters is a fundamen...
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01/05/2022
RobustCalibration: Robust Calibration of Computer Models in R
Two fundamental research tasks in science and engineering are forward pr...
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10/22/2021
Characterizing Performance Inequity Across U.S. Ookla Speedtest Users
The Internet has become indispensable to daily activities, such as work,...
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08/13/2021
Efficient force field and energy emulation through partition of permutationally equivalent atoms
Kernel ridge regression (KRR) that satisfies energy conservation is a po...
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05/03/2021
Uncertainty quantification and estimation in differential dynamic microscopy
Differential dynamic microscopy (DDM) is a form of video image analysis ...
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11/21/2020
Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data
We introduce the Gaussian orthogonal latent factor processes for modelin...
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09/30/2020
Emulating the First Principles of Matter: A Probabilistic Roadmap
This chapter provides a tutorial overview of first principles methods to...
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10/27/2018
Calibration of imperfect mathematical models by multiple sources of data with measurement bias
Model calibration involves using experimental or field data to estimate ...
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08/31/2018
Generalized probabilistic principal component analysis of correlated data
Principal component analysis (PCA) is a well-established tool in machine...
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07/27/2018
Nonparametric estimation of utility functions
Inferring a decision maker's utility function typically involves an elic...
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07/10/2018
A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration
The Gaussian stochastic process (GaSP) is a useful technique for predict...
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04/25/2018
Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection
Gaussian stochastic process (GaSP) has been widely used in two fundament...
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01/05/2018
RobustGaSP: Robust Gaussian Stochastic Process Emulation in R
Gaussian stochastic process (GaSP) emulation is a powerful tool for appr...
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11/30/2017