My Research:
I work in the field of Bayesian statistics, with current primary emphasis on
computer models (e.g., spatial inverse problems, simulator emulation,
adaptive experimental design, optimization), and past work on connections
between statistics and machine learning.
Publications

Equivalence Testing for Multiple Groups, with Tony Pourmohamad
(Stat, 2024, Volume 13, Number 1)
 A Neutral Zone Classifier for Three Classes with an
Application to Text Mining, with Dylan Friel, Yunzhe Li, Benjamin
Ellis, Daniel Jeske, and Philip Kass
(Statistical Analysis and Data Mining, 2023, Volume 16, Number
3, pp. 560568)

Stochastic Collapsed Variational Inference for Structured Gaussian
Process Regression Networks
with Rui Meng and Kristofer Bouchard (In Classification and Data Science
in the Digital Age (Proceedings of the 17th Conference of the
International Federation of Classification Societies),
pp. 253261)
 Hierarchical
ContinuousTime Inhomogeneous Hidden Markov Model
for Cancer Screening with Extensive Followup Data with
Rui Meng, B. Soper, J. Nygard, and M. Nygard
(Statistical Methods in Medical Research, 2023, Volume 31,
Number 12, pp. 23832399)
 Bayesian Optimization Via Barrier Functions, with Tony Pourmohamad
(Journal of Computational and Graphical Statistics, 2021, Volume 31,
Number 1, pp. 7483)
 Random field models for
spatial smoothing of airborne lidar transect data with Amanda
Coleman and Richard Olsen (In Proc. SPIE 11744, Laser Radar
Technology and Applications XXVI, 117440G, April 12, 2021).
 Specification of Basis Spacing for Process Convolution Gaussian
Process Models
with Waley W. J. Liang (In Data Analysis and Rationality in a
Complex World (Proceedings of the 16th Conference of the
International Federation of Classification Societies),
pp. 141148; this version is TR UCSCSOE1911)
 Nonstationary Multivariate Gaussian Processes for Electronic
Health Records, with Rui Meng, B. Soper, V. Liu, J. Greene, and P. Ray.
(Journal of Biomedical Informatics, 2021, Volume 117,
Article 103698)
 The Statistical Filter Approach to Constrained Optimization
with Tony Pourmohamad (Technometrics, 2020, Volume 62, Issue 3,
pp. 303312; this version is TR UCSCSOE1814)
 Bayesian Nonstationary Gaussian Process Models via Treed Process
Convolutions with Waley Liang (Advances in
Data Analysis and Classification, 2019, Volume 13, Number 3,
pp. 797818; this version is TR
UCSCSOE1125)
 RealTime Detection Of InFlight Aircraft Damage
with Brenton Blair and Misty Davies (Journal of Classification,
2017, Volume 34, Number 3, pp. 494523; this version is TR UCSCSOE1603)
 Graphical Jump
Method for Neural Networks with Jing Chang (Journal of Data
Science, 2017, Volume 15, Number 4, pp. 669690)
 A Robust Algorithm for Optimum Utility with John Guenther (Journal of Advances in Applied Mathematics, 2017, Volume 2, Number 1, pp. 114)
 Cluster Search Algorithm for Finding Multiple Optima with John Guenther (Applied Mathematics, 2016, Volume 7, Number 7, pp. 736752)

Multivariate Stochastic Process Models for Correlated Responses of Mixed Type with Tony Pourmohamad (Bayesian Analysis, 2016, Volume 11, Number 3, pp. 797820)
 Modeling an Augmented Lagrangian for
Blackbox Constrained Optimization (with discussion) with Robert Gramacy, Genetha
Gray, Sebastien Le Digabel, Pritam Ranjan, Garth Wells, and Stefan Wild
(Technometrics, 2016, Volume 58, Issue 1, pp. 111)
 Sequential Design for Achieving Estimated Accuracy of Global Sensitivities
with John Guenther and Genetha Gray (Applied Stochastic Models in Business and
Industry, 2015, Volume 31, Issue 6, pp. 782800; this version is TR 2013  TR UCSCSOE1317)
 Variable Selection via a
Multistage Strategy with Jing Chang (Journal of Applied
Statistics, 2015, Volume 42, Number 4, pp. 762774)
 Optimization Under Constraints by Applying an Asymmetric Entropy
Measure with David Lindberg (Journal of Computational and Graphical
Statistics, 2015, Volume 24, Number 2, pp. 379393; this version is TR UCSCSOE1306)
 Sequential Process Convolution Gaussian Process Models via Particle Learning with Waley Liang (Statistics and Its Interface, 2014, Volume 7, Number 4, pp. 465475; this version is TR UCSCSOE1209)
 Finding and Choosing Among Multiple Optima with John Guenther and
Genetha Gray (Applied Mathematics, 2014, Volume 5, Number 2, pp. 300317)
 Modeling and Anomalous Cluster Detection for Point Processes Using Process Convolutions with Waley Liang,
Jacob Colvin, and Bruno Sansó (Journal of
Computational and Graphical Statistics, 2014, Volume 23, Number 1, pp. 129150; this version is TR UCSCSOE1009)
 Gaussian Process Modeling of Derivative Curves with Tracy Holsclaw,
Bruno Sansó, David Higdon, Katrin Heitmann, Ujjaini Alam, and
Salman Habib (to appear in Technometrics, 2013; this version is TR UCSCSOE1102)

Simultaneous Optimization and Uncertainty Quantification with
Genetha Gray and John Guenther (Journal of Computational Methods in Sciences and Engineering, 2012, Volume 12, Number 12, pp. 99110)

Bagging During Markov Chain Monte Carlo for Smoother Predictions
(Antarctica Journal of Mathematics, 2013, Volume 10, Number 5,
Paper 4, pp. 447451), an earlier version is TR UCSCSOE1309

Cases for the Nugget in Modeling Computer Experiments with
Robert Gramacy (Statistics and Computing, 2012, Volume 22, pp. 713722)
 Optimization Subject to Hidden Constraints via Statistical
Emulation with Robert Gramacy, Crystal Linkletter, and Genetha
Gray (Pacific Journal of Optimization, 2011, Volume 7, pp. 467478;
this version is TR UCSCSOE1010)
 Nonparametric Reconstruction of
the Dark Energy Equation of State from Diverse Data Sets with
Tracy Holsclaw, Bruno Sansó, David Higdon, Katrin Heitmann,
Ujjaini Alam, and Salman Habib (Physical Review D, 2011, Volume 82,
083501)

Optimization Under Unknown Constraints with Robert Gramacy
(2011, in Bayesian Statistics 9 with discussion, pp. 229256)
 Exploring the Effect of Weight Misspecification on Flight Prediction,
with Jing Chang (Advances and Applications in Statistical Sciences,
2011, Volume 6, Issue 1, pp. 2738)

Designing and Analyzing a Circuit Device Experiment Using
Treed Gaussian Processes,
with Matthew Taddy, Robert Gramacy, and Genetha
Gray, in The Oxford Handbook of Applied Bayesian Analysis, 2010

Nonparametric Dark Energy Reconstruction from Supernova Data
with Tracy Holsclaw, Ujjaini Alam, Bruno Sansó, Katrin Heitmann,
Salman Habib and David Higdon (Physical Review
Letters, 2010, Volume 105, 241302)

Nonparametric Reconstruction of the Dark Energy Equation of
State with Tracy Holsclaw, Ujjaini Alam, Bruno Sansó, Katrin
Heitmann, Salman Habib and David Higdon (Physical Review D,
2010, Volume 82, 103502)
 Selection of a Representative Sample
with Matthew Taddy and Genetha Gray (Journal of Classification,
2010, Volume 27, pp. 4153; this version is UCSC TR SOE0812)
 Bayesian Guided Pattern Search for Robust Local Optimization
with Matthew Taddy, Genetha Gray, and Joshua Griffin
(Technometrics, 2009, Volume 51, pp. 389401; this version is
UCSC TR ams200802)
 Fast Inference for Statistical Inverse Problems
with Matthew Taddy and Bruno Sansó (Inverse
Problems, 2009, Volume 25, 085001; also available is the older
version, UCSC TR ams200803)

Adaptive Design and Analysis of Supercomputer Experiments with
Robert Gramacy (Technometrics, 2009, Volume 51, pp. 130145)
 Bayesian Treed Gaussian
Process Models with an Application to
Computer Modeling with Robert Gramacy (Journal of the
American Statistical Association, 2008, Volume 103, pp. 11191130)
 Inference
for a Proton Accelerator Using Convolution Models with
Bruno Sansó, Weining Zhou, and Dave Higdon (Journal of the
American Statistical Association, 2008, Volume 103, pp. 604613; this
version is UCSC TR ams200531)
 Gaussian Processes and
Limiting Linear Models with Robert Gramacy (Computational
Statistics and Data Analysis, 2008, Volume 53, pp. 123136)
 Chocolate Chip Cookies as a Teaching
Aid (The
American Statistician 2007, Volume 61, Issue 4, pp. 351355)
 Multiscale
Modeling: A Bayesian Perspective. With Marco Ferreira (2007)
 Default Priors for Neural Network Classification
(Journal of Classification, 2007, pp. 5370; this
version is UCSC TR ams200515)
 Multiresolution
Genetic Algorithms and Markov Chain Monte Carlo with Chris
Holloman and Dave Higdon (Journal of Computational and
Graphical Statistics 2006, pp. 861879; this version is Duke ISDS TR #0206)

MultiScale and Hidden Resolution Time Series Models with Marco
A. R. Ferreira, Mike West, and Dave Higdon (Bayesian Analysis,
2006, pp. 947968)
 Inferring
Particle Distribution in a Proton Accelerator Experiment with
Bruno Sansó, Weining Zhou, and Dave Higdon (Bayesian
Analysis, 2006, pp. 249264)
 Neural Networks and Default Priors
(Proceedings of the American Statistical Association, Section on
Bayesian Statistical Science, 2005)
 Efficient
Models for Correlated Data via Convolutions of Intrinsic Processes
with Dave Higdon, Kate Calder, and Chris Holloman (Statistical Modelling, 2005; this version is UCSC TR ams200403)
 Bayesian Nonparametrics
via Neural Networks. (2004)
 Priors
for Neural Networks (2004, in Classification, Clustering, and
Data Mining Applications, pp. 141150; this version is UCSC TR ams200309)
 Parameter Space Exploration With Gaussian Process
Trees with Robert Gramacy and William Macready (2004, in
Proceedings of the International Conference on Machine
Learning, pp. 353360)
 Lossless
Online Bayesian Bagging with Merlise Clyde (Journal of Machine
Learning Research, February 2004)
 Markov
chain Monte Carlobased approaches for inference in computationally
intensive inverse problems with Dave Higdon and Chris Holloman
(2003, in Bayesian Statistics 7, pp. 181197; this version is
Duke ISDS #0210)
 Multiscale
Modeling of 1D Permeability Fields with Marco A. R. Ferreira,
Zhuoxin Bi, Mike West, and Dave Higdon (2003, in Bayesian
Statistics 7, pp. 519527; this version is Duke ISDS TR #0208)
 A
Noninformative Prior for Neural Networks (Machine
Learning, 2003; this version is Duke ISDS TR #0004)
 Markov Random
Field Models for HighDimensional Parameters in Simulations of Fluid
Flow in Porous Media with David Higdon, Zhuoxin Bi, Marco
Ferreira, and Mike West (Technometrics, August 2002; this
version is Duke ISDS #0035), a version of which
also won Best Contributed Paper in the Statistical Computing Section
sessions at the 2000 Joint Statistical Meetings
 A Bayesian
Approach to Characterizing Uncertainty in Inverse Problems Using
Coarse and Fine Scale Information with Dave Higdon and Zhuoxin Bi
(IEEE Transactions on Signal Processing, February 2002; this
version is Duke ISDS TR #0102)
 Did Lennox
Lewis Beat Evander Holyfield? Methods for Analyzing Smallsample
Interrater Agreement Problems with Daniel
Cork and David Algranati (The Statistician, July 2002; this version is CMU Stats TR #732)
 Difficulties in Estimating the Normalizing Constant of the
Posterior for a Neural Network (Journal of Computational and
Graphical Statistics, March 2002)
 Model
Selection for Neural Network Classification (Journal of
Classification, 2001; this version is Duke ISDS TR #0018)
 Bagging and
the Bayesian Bootstrap with Merlise Clyde (In Artificial
Intelligence and Statistics 2001, T. Richardson and T. Jaakkola
eds.; this version is Duke ISDS TR #0034)
 Loglinear Models and
GoodnessofFit Statistics for Train Waybill Data, with Kert Viele
(Journal of Transportation and Statistics, April 2001)
 Consistency
of Posterior Distributions for Neural Networks (Neural
Networks, July 2000; this version is CMU Stats TR #676, 1998)
 Model
Selection and Model Averaging for Neural Network Regression
(Proceedings of the American Statistical Association, Section on
Bayesian Statistical Science, 1999; this version is Duke ISDS TR #0032)
Technical Reports
Last modified on December 27, 2023.