30 citations · 170 across the 22 of their papers we have counts for
22 papers
GNet: A scalable and flexible Gaussian process network with nonparametric neurons
Mengyang Gu
We build GNet, a scalable and flexible Gaussian process network with nonparametric activation functions. The key computational contribution is to make estimation of GP activations…
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
Mengyang Gu, Yizi Lin, Victor Chang Lee +1
Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assess…
A Nonparametric Mixed-Effects Mixture Model for Patterns of Clinical Measurements Associated with COVID-19
Xiaoran Ma, Wensheng Guo, Mengyang Gu +3
Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asy…
Data-driven model construction for anisotropic dynamics of active matter
Mengyang Gu, Xinyi Fang, Yimin Luo
The dynamics of cellular pattern formation is crucial for understanding embryonic development and tissue morphogenesis. Recent studies have shown that human dermal fibroblasts cult…
Molecular-scale substrate anisotropy and crowding drive long-range nematic order of cell monolayers
Yimin Luo, Mengyang Gu, Minwook Park +7
The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. Elongated cells can co-align due to steri…
Reliable emulation of complex functionals by active learning with error control
Xinyi Fang, Mengyang Gu, Jianzhong Wu
A statistical emulator can be used as a surrogate of complex physics-based calculations to drastically reduce the computational cost. Its successful implementation hinges on an acc…
Scalable marginalization of correlated latent variables with applications to learning particle interaction kernels
Mengyang Gu, Xubo Liu, Xinyi Fang +1
Marginalization of latent variables or nuisance parameters is a fundamental aspect of Bayesian inference and uncertainty quantification. In this work, we focus on scalable marginal…
RobustCalibration: Robust Calibration of Computer Models in R
Mengyang Gu
Two fundamental research tasks in science and engineering are forward predictions and data inversion. This article introduces a recent R package RobustCalibration for Bayesian data…
Characterizing Performance Inequity Across U.S. Ookla Speedtest Users
Udit Paul, Jiamo Liu, Vivek Adarsh +3
The Internet has become indispensable to daily activities, such as work, education and health care. Many of these activities require Internet access data rates that support real-ti…
Efficient force field and energy emulation through partition of permutationally equivalent atoms
Hao Li, Musen Zhou, Jessalyn Sebastian +2
Gaussian process (GP) emulator has been used as a surrogate model for predicting force field and molecular potential, to overcome the computational bottleneck of molecular dynamics…
Uncertainty quantification and estimation in differential dynamic microscopy
Mengyang Gu, Yimin Luo, Yue He +2
Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broa…
Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data
Mengyang Gu, Hanmo Li
We introduce Gaussian orthogonal latent factor processes for modeling and predicting large correlated data. To handle the computational challenge, we first decompose the likelihood…
Emulating the First Principles of Matter: A Probabilistic Roadmap
Jianzhong Wu, Mengyang Gu
This chapter provides a tutorial overview of first principles methods to describe the properties of matter at the ground state or equilibrium. It begins with a brief introduction t…
Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias
Mengyang Gu, Kyle Anderson, Erika McPhillips
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in…
Generalized probabilistic principal component analysis of correlated data
Mengyang Gu, Weining Shen
Principal component analysis (PCA) is a well-established tool in machine learning and data processing. The principal axes in PCA were shown to be equivalent to the maximum marginal…
Nonparametric estimation of utility functions
Mengyang Gu, Debarun Bhattacharjya, Dharmashankar Subramanian
Inferring a decision maker's utility function typically involves an elicitation phase where the decision maker responds to a series of elicitation queries, followed by an estimatio…
A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration
Mengyang Gu, Fangzheng Xie, Long Wang
Model calibration or data inversion is one of fundamental tasks in uncertainty quantification. In this work, we study the theoretical properties of the scaled Gaussian stochastic p…
Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection
Mengyang Gu
Gaussian stochastic process (GaSP) has been widely used in two fundamental problems in uncertainty quantification, namely the emulation and calibration of mathematical models. Some…
RobustGaSP: Robust Gaussian Stochastic Process Emulation in R
Mengyang Gu, Jesús Palomo, James O. Berger
Gaussian stochastic process emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a ch…
Fast Nonseparable Gaussian Stochastic Process with Application to Methylation Level Interpolation
Mengyang Gu, Yanxun Xu
Gaussian stochastic process (GaSP) has been widely used as a prior over functions due to its flexibility and tractability in modeling. However, the computational cost in evaluating…
Robust Gaussian Stochastic Process Emulation
Mengyang Gu, Xiaojing Wang, James O. Berger
We consider estimation of the parameters of a Gaussian Stochastic Process (GaSP), in the context of emulation (approximation) of computer models for which the outcomes are real-val…
Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction
Mengyang Gu, Long Wang
We consider the problem of calibrating an imperfect computer model using experimental data. To compensate the misspecification of the computer model and make more accurate predicti…