My Research:

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, to appear)
• 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. 253-261)
• Hierarchical Continuous-Time 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. 2383-2399)
• Bayesian Optimization Via Barrier Functions, with Tony Pourmohamad (Journal of Computational and Graphical Statistics, 2021, Volume 31, Number 1, pp. 74-83)
• 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. 141-148; this version is TR UCSC-SOE-19-11)
• 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. 303-312; this version is TR UCSC-SOE-18-14)
• Bayesian Nonstationary Gaussian Process Models via Treed Process Convolutions with Waley Liang (Advances in Data Analysis and Classification, 2019, Volume 13, Number 3, pp. 797-818; this version is TR UCSC-SOE-11-25)
• Real-Time Detection Of In-Flight Aircraft Damage with Brenton Blair and Misty Davies (Journal of Classification, 2017, Volume 34, Number 3, pp. 494-523; this version is TR UCSC-SOE-16-03)
• Graphical Jump Method for Neural Networks with Jing Chang (Journal of Data Science, 2017, Volume 15, Number 4, pp. 669-690)
• A Robust Algorithm for Optimum Utility with John Guenther (Journal of Advances in Applied Mathematics, 2017, Volume 2, Number 1, pp. 1-14)
• Cluster Search Algorithm for Finding Multiple Optima with John Guenther (Applied Mathematics, 2016, Volume 7, Number 7, pp. 736-752)
• Multivariate Stochastic Process Models for Correlated Responses of Mixed Type with Tony Pourmohamad (Bayesian Analysis, 2016, Volume 11, Number 3, pp. 797-820)
• 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. 1-11)
• 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. 782-800; this version is TR 2013 - TR UCSC-SOE-13-17)
• Variable Selection via a Multi-stage Strategy with Jing Chang (Journal of Applied Statistics, 2015, Volume 42, Number 4, pp. 762-774)
• Optimization Under Constraints by Applying an Asymmetric Entropy Measure with David Lindberg (Journal of Computational and Graphical Statistics, 2015, Volume 24, Number 2, pp. 379-393; this version is TR UCSC-SOE-13-06)
• Sequential Process Convolution Gaussian Process Models via Particle Learning with Waley Liang (Statistics and Its Interface, 2014, Volume 7, Number 4, pp. 465-475; this version is TR UCSC-SOE-12-09)
• Finding and Choosing Among Multiple Optima with John Guenther and Genetha Gray (Applied Mathematics, 2014, Volume 5, Number 2, pp. 300-317)
• 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. 129-150; this version is TR UCSC-SOE-10-09)
• 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 UCSC-SOE-11-02)
• Simultaneous Optimization and Uncertainty Quantification with Genetha Gray and John Guenther (Journal of Computational Methods in Sciences and Engineering, 2012, Volume 12, Number 1-2, pp. 99-110)
• Bagging During Markov Chain Monte Carlo for Smoother Predictions (Antarctica Journal of Mathematics, 2013, Volume 10, Number 5, Paper 4, pp. 447-451), an earlier version is TR UCSC-SOE-13-09
• Cases for the Nugget in Modeling Computer Experiments with Robert Gramacy (Statistics and Computing, 2012, Volume 22, pp. 713-722)
• Optimization Subject to Hidden Constraints via Statistical Emulation with Robert Gramacy, Crystal Linkletter, and Genetha Gray (Pacific Journal of Optimization, 2011, Volume 7, pp. 467-478; this version is TR UCSC-SOE-10-10)
• 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. 229-256)
• Exploring the Effect of Weight Misspecification on Flight Prediction, with Jing Chang (Advances and Applications in Statistical Sciences, 2011, Volume 6, Issue 1, pp. 27-38)
• 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. 41-53; this version is UCSC TR SOE-08-12)
• Bayesian Guided Pattern Search for Robust Local Optimization with Matthew Taddy, Genetha Gray, and Joshua Griffin (Technometrics, 2009, Volume 51, pp. 389-401; this version is UCSC TR ams2008-02)
• 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 ams2008-03)
• Adaptive Design and Analysis of Supercomputer Experiments with Robert Gramacy (Technometrics, 2009, Volume 51, pp. 130-145)
• Bayesian Treed Gaussian Process Models with an Application to Computer Modeling with Robert Gramacy (Journal of the American Statistical Association, 2008, Volume 103, pp. 1119-1130)
• 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. 604-613; this version is UCSC TR ams2005-31)
• Gaussian Processes and Limiting Linear Models with Robert Gramacy (Computational Statistics and Data Analysis, 2008, Volume 53, pp. 123-136)
• Chocolate Chip Cookies as a Teaching Aid (The American Statistician 2007, Volume 61, Issue 4, pp. 351-355)
• Multiscale Modeling: A Bayesian Perspective. With Marco Ferreira (2007)
• Default Priors for Neural Network Classification (Journal of Classification, 2007, pp. 53-70; this version is UCSC TR ams2005-15)
• Multi-resolution Genetic Algorithms and Markov Chain Monte Carlo with Chris Holloman and Dave Higdon (Journal of Computational and Graphical Statistics 2006, pp. 861-879; this version is Duke ISDS TR #02-06)
• Multi-Scale and Hidden Resolution Time Series Models with Marco A. R. Ferreira, Mike West, and Dave Higdon (Bayesian Analysis, 2006, pp. 947-968)
• Inferring Particle Distribution in a Proton Accelerator Experiment with Bruno Sansó, Weining Zhou, and Dave Higdon (Bayesian Analysis, 2006, pp. 249-264)
• 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 ams2004-03)
• Bayesian Nonparametrics via Neural Networks. (2004)
• Priors for Neural Networks (2004, in Classification, Clustering, and Data Mining Applications, pp. 141-150; this version is UCSC TR ams2003-09)
• Parameter Space Exploration With Gaussian Process Trees with Robert Gramacy and William Macready (2004, in Proceedings of the International Conference on Machine Learning, pp. 353-360)
• Lossless Online Bayesian Bagging with Merlise Clyde (Journal of Machine Learning Research, February 2004)
• Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems with Dave Higdon and Chris Holloman (2003, in Bayesian Statistics 7, pp. 181-197; this version is Duke ISDS #02-10)
• Multi-scale Modeling of 1-D Permeability Fields with Marco A. R. Ferreira, Zhuoxin Bi, Mike West, and Dave Higdon (2003, in Bayesian Statistics 7, pp. 519-527; this version is Duke ISDS TR #02-08)
• A Noninformative Prior for Neural Networks (Machine Learning, 2003; this version is Duke ISDS TR #00-04)
• Markov Random Field Models for High-Dimensional 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 #00-35), 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 #01-02)
• Did Lennox Lewis Beat Evander Holyfield? Methods for Analyzing Small-sample Inter-rater 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 #00-18)
• 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 #00-34)
• Loglinear Models and Goodness-of-Fit 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 #00-32)

Technical Reports

• Determining Convergence in Gaussian Process Surrogate Model Optimization with Nick Grunloh (2015 - TR UCSC-SOE-15-05)
• Predicting Variable-Length Functional Outputs for Emulation of a Flight Simulator with Yuning He and Misty Davies (2012 - TR UCSC-SOE-12-16)
• Bayesian Treed Gaussian Process Models with Robert Gramacy (2006 - UCSC TR ams2006-08)
• Adaptive Exploration of Computer Experiment Parameter Spaces with Robert Gramacy and William Macready (2005 - UCSC TR ams2005-16)
• Multi-scale Random Field Models with Marco Ferreira, Dave Higdon, and Mike West (2005 -- Duke ISDS #05-02)
• Upscaling Tensorial Permeability Fields Based on Gaussian Markov Random Field Models and the Hybrid Mixed Finite Element Method with Zhuoxin Bi, John Trangenstein, and Dave Higdon (2005 -- UCSC TR ams2005-14)
• Flexible Gaussian Processes via Convolution with Chris Holloman, Kate Calder, and Dave Higdon (2002 -- Duke ISDS #02-09)
• Model Selection for Consumer Loan Application Data (1996 -- CMU Stats TR #650)