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06/13/2023
ACE: Active Learning for Causal Inference with Expensive Experiments
Experiments are the gold standard for causal inference. In many applicat...
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06/11/2023
Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma
The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theori...
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02/01/2023
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
In many areas of science and engineering, computer simulations are widel...
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11/01/2022
Stacking designs: designing multi-fidelity computer experiments with confidence
In an era where scientific experiments can be very costly, multi-fidelit...
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09/27/2022
Multi-Stage Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions
In an era where scientific experimentation is often costly, multi-fideli...
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03/08/2022
PERCEPT: a new online change-point detection method using topological data analysis
Topological data analysis (TDA) provides a set of data analysis tools fo...
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09/15/2021
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales
Algorithmic harmonization - the automated harmonization of a musical pie...
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07/31/2021
A graphical Gaussian process model for multi-fidelity emulation of expensive computer codes
We present a novel Graphical Multi-fidelity Gaussian Process (GMGP) mode...
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07/09/2021
Gaussian Process Subspace Regression for Model Reduction
Subspace-valued functions arise in a wide range of problems, including p...
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02/27/2021
Online High-Dimensional Change-Point Detection using Topological Data Analysis
Topological Data Analysis (TDA) is a rapidly growing field, which studie...
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02/10/2021
Sequential change-point detection for mutually exciting point processes over networks
We present a new CUSUM procedure for sequentially detecting change-point...
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01/17/2021
TSEC: a framework for online experimentation under experimental constraints
Thompson sampling is a popular algorithm for solving multi-armed bandit ...
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01/05/2021
Bayesian Uncertainty Quantification for Low-rank Matrix Completion
We consider the problem of uncertainty quantification for an unknown low...
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12/26/2020
Population Quasi-Monte Carlo
Monte Carlo methods are widely used for approximating complicated, multi...
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06/12/2020
Uncertainty Quantification for Inferring Hawkes Networks
Multivariate Hawkes processes are commonly used to model streaming netwo...
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04/29/2020
Energy Balancing of Covariate Distributions
Bias in causal comparisons has a direct correspondence with distribution...
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11/17/2019
A hierarchical expected improvement method for Bayesian optimization
Expected improvement (EI) is one of the most popular Bayesian optimizati...
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11/14/2019
Distributional Clustering: A distribution-preserving clustering method
One key use of k-means clustering is to identify cluster prototypes whic...
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10/12/2019
Adaptive design for Gaussian process regression under censoring
A key objective in engineering problems is to predict an unknown experim...
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10/03/2019
Function-on-function kriging, with applications to 3D printing of aortic tissues
3D-printed medical phantoms, which use synthetic metamaterials to mimic ...
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08/23/2019
BdryGP: a new Gaussian process model for incorporating boundary information
Gaussian processes (GPs) are widely used as surrogate models for emulati...
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02/24/2018
Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics
This interdisciplinary study, which combines machine learning, statistic...
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12/10/2017
Analysis-of-marginal-Tail-Means - a new method for robust parameter optimization
This paper presents a novel method, called Analysis-of-marginal-Tail-Mea...
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12/08/2017
Maximum entropy low-rank matrix recovery
We propose a novel, information-theoretic method, called MaxEnt, for eff...
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07/26/2017