2024

• Voronoi candidates for Bayesian optimization (2023) with Nate Wycoff, John Smith and Annie Booth; preprint on arXiv:2402.04922
• Publishing an applied statistics paper: Guidance and advice from aditors (2024) with Christine Anderson-Cook, Lu Lu, Allison Jones-Farmer, Doug Montgomery and Bill Woodall; Quality and Reliability Engineering International, DOI: 10.1002/qre.3501.
• Actively learning deep Gaussian process models for failure contour and probability estimation (2024) with Annie Sauer and Ashwin Renganathan; AIAA 2024-0577 SCITECH Forum.

2023

• Vecchia-approximated deep Gaussian processes for computer experiments (2023) with Annie Sauer and Andrew Cooper. Journal of Computational and Graphical Statistics, 32(3); preprint on arXiv:2204.02904
• Contour location for reliability in airfoil simulation experiments using deep Gaussian processes (2023) with Annie Booth and Ashwin Renganathan; preprint on arXiv:2308.04420
• Traditional kriging versus modern Gaussian processes for large-scale mining data (2023) with Ryan Christianson and Ryan Pollyea. Statistical Analysis and Data Mining 16(5), pp. 488-506; preprint on arXiv:2207.10138
• Non-stationary Gaussian process surrogates (2023) with Annie Sauer and Andrew Cooper; preprint on arXiv:2305.19242
• Robust expected improvement for Bayesian optimization (2023) with Ryan Christianson. IISE Transactions; preprint on arXiv:2302.08612
• Active learning of deep Gaussian process surrogates (2023) with Annie Sauer and Dave Higdon. Technometrics, 65(1), pp. 4-18; preprint on arXiv:2012.08015
• Augmenting a simulation campaign for hybrid computer model and field data experiments (2023) with Scott Koermer, Justin Loda, and Aaron Noble. Technometrics, to appear; preprint on arXiv:2301.10228
• Active learning of piecewise Gaussian process surrogates (2023) with Chiwoo Park, Robert Waelder, Bonggwon Kang, Benji Maruyama, Soondo Hong; preprint on arXiv:2301.08789
• Entropy-based adaptive design for contour finding and estimating reliability (2023) with Austin Cole, James E. Warner, Geoffrey F. Bomarito, Patrick E. Leser, William P. Leser. Journal of Quality Technology, 55(1), pp. 70-87; preprint on arXiv:2105.11357

2022

• Triangulation candidates for Bayesian optimization (2022) with Annie Sauer and Nate Wycoff. 36th Conference on Neural Information Processing Systems (NeurIPS); preprint on arXiv:2112.07457
• How to write a Technometrics paper, an unoffical tutorial (2022) with periodic updates
• Discussion of paper by Marmin & Filippone (2022) with Annie Sauer. An invited discussion of Deep Gaussian Processes for Calibration of Computer Models by S. Marmin and M. Filippone (2022). Bayesian Analysis, pp. 1-30
• Multi-output calibration of a honeycomb seal via on-site surrogates (2022) with Jiangeng Huang. Technometrics, 64(4), 548-563; preprint on arXiv:2102.00391
• Batch-sequential design and heteroskedastic surrogate modeling for delta smelt conservation (2022) with Boya Zhang, Leah Johnson, Eric Smith, and Kenny Rose. Annals of Applied Statistics, 16(2), pp. 816-842; preprint on arXiv:2010.06515
• Large-scale local surrogate modeling of stochastic simulation experiments (2022) with Austin Cole and Mike Ludkovski. Computational Statistics and Data Analysis, 174; preprint on arXiv:2109.05324
• A statistical evaluation of multiple regression models for contact dynamics in rail vehicles using roller rig data (2022) Sayed Mohammad Hosseini, Ahmad Radmehr, Arash Ahangarnejad and Medhi Ahmadian. International Journal of Rail Transportation, 10(6), pp. 717-729
• Massive Parallelization (2022) In Piegorsch, W.W., Levine, R.A., Zhang, H.H., and Lee, T.C.M. (eds.). Computational Statistics in Data Science, pp. 537-557. Chichester: John Wiley & Sons. ISBN: 978-1-119-56107-1
• Analyzing stochastic computer models: an overview with opportunities (2022) with Evan Baker, Pierre Barbillon, Arindam Fadikar, Radu Herbei, David Higdon, Jiangeng Huang, Leah R. Johnson, Anirban Mondal, Bianica Pires, Jerome Sacks, Vadim Sokolov. Statistical Science, 37(1), pp. 64-89; preprint on arXiv:2002.01321
• Sensitivity prewarping for local surrogate modeling (2022) with Nate Wycoff and Mickael Binois. Technometrics, 64(4), pp. 535-547; preprint on arXiv:2101.06296

2021

• hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) with Mickael Binois. Journal of Statistical Software 98(13); provided as a vignette in the hetGP package
• Precision aggregated local models (2021) with Adam Edwards. Statistical Analysis and Data Mining, 14, p0. 676-697; preprint on arXiv:2005.13375
• Locally induced Gaussian processes for large-scale simulation experiments (2021) with Austin Cole and Ryan Christianson. Statistics and Computing, 33(1); preprint on arXiv:2008.12857
• Distance-distributed design for Gaussian process surrogates (2021) with Boya Zhang and Austin Cole. Technometrics, 63(1), pp. 40-52; preprint on arXiv:1812.02794

2020

• Assessing Bayes factor surfaces using interactive visualization and computer surrogate modeling (2020) with Chris Franck. The American Statistician, 74(4), pp. 359-269; preprint on arXiv:1809.05580
• Discussion of paper by Bhadra, et al. Horseshoe regularization for machine learning in complex and deep models by A. Bhadra, J. Datta, Y. Li, N.G. Polson (2020). International Statistical Review, 88 (2), pp. 326-329
• On-site surrogates for large-scale calibration (2020) with Jiangeng Huang, Mickael Binois, and Mirko Librashi. Applied Stochastic Models in Business and Industry, 36(2), pp. 283-304; preprint on arXiv:1810.01903
• Surrogates: Gaussian process modeling, design and optimization for the applied sciences (2020). Chapman Hall/CRC, Boca Raton, FL
• A shiny update to an old experiment game. The American Statistician, 74(1), pp. 87-92; preprint on arXiv:1803.00613

2019

• An open challenge to advance probabilistic forecasting for dengue epidemics (2019) with Michael A. Johansson and 50+ others. Proceedings of the National Academy of Sciences, published November 2019
• Methods for Analyzing Large Spatial Data: A Review and Comparison (2019) with Matthew J. Heaton, Abhirup Datta, Andrew Finley, Reinhard Furrer, Joe Guinness, Rajarshi Guhaniyogi, Florian Gerber, Dorit Hammerling, Matthias Katzfuss, Finn Lindgren, Douglas W. Nychka, Furong Sun, Andrew Zammit-Mangion. Journal of Agricultural, Biological and Environmental Statistics, 24(3), pp. 398-425; preprint on arXiv:1710.05013
• Synthesizing simulation and field data of solar irradiance (2019) with Furong Sun, Benjamin Haaland, Siyuan Lu and Youngdeok Hwang. Statistical Analysis and Data Mining, 12(4), pp. 311-324; preprint on arXiv:1806.05131. Solar irradiance movie.
• Parameter and uncertainty estimation for dynamical systems using surrogate stochastic processes (2019) with Matthias Chung, Mickael Binois, John Bardsley, David Moquin, Amanda Smith and Amber Smith. SIAM Journal on Scientific Computing, 41(4), pp. A2212-2238; preprint on arXiv:1802.00852
• Replication or exploration? Sequential design for stochastic simulation experiments (2019) with Mickael Binois, Jiangeng Huang and Mike Ludkovski. Technometrics, 61(1), pp. 7-23; preprint on arXiv:1710.03206. This paper won the Youden Award in 2020. Here is a talk I gave at DSSV 2020.
• Emulating satellite drag from large simulation experiments (2019) with Furong Sun, Benjamin Haaland, Earl Lawrence and Andrew Walker. SIAM/ASA Journal on Uncertainty Quantification, 7(2), pp. 720-759; preprint on arXiv:1712.00182
• Book review: Computer age statistical inference: Algorithms, evidence, and data science, by Bradley Efron and Trevor Hastie. (2019) Bulletin of the American Mathematical Society, 56(1), pp. 137-142.

2018

• Practical heteroskedastic Gaussian process modeling for large simulation experiments (2018) with Mickael Binois and Mike Ludkovski. Journal of Computational and Graphical Statstics, 27(4), pp. 808-821; preprint on arXiv:1611.05902
• Potentially predictive variance reducing subsample locations in local Gaussian process regression (2018) with Chih-Li Sung and Benjamin Haaland. Statistica Sinica 28, pp. 577-600; preprint on arXiv:1604.04980
• Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study (2018) with Leah Johnson and several others. Annals of Applied Statistics, 12(1), pp. 27-66; preprint on arXiv:1702.00261

2017

• Anomaly detection in large-scale wind tunnel tests using Gaussian processes. (2017) with Ian Crandell, Anthony J. Millican, Robert Vasta, Scotland Leman, Eric Smith, Nathan Alexander, William Devenport, and Mickael Binois. 33rd AIAA Aerodynamic Measurement Technology and Ground Testing Conference.
• Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models (2017) with Tim Graves, Christian Franzke, Nicholas Watkins and Elizabeth Trindale. Physica A, 473, 60-71
• A brief history of long memory: Hurst, Mandelbrot and the Road to ARFIMA, 1951–1980 (2017) with Tim Graves, Christian Franzke and Nicholas Watkins. Entropy 19(9); preprint on arXiv:1406.6018; also see our Chicago Booth Review article

2016

• Comment on article by Pratola. An invited discussion of Efficient Metropolis–Hastings Proposal Mechanisms for Bayesian Regression Tree Models by M. Pratola (2016). Bayesian Analysis, 11(3), pp. 913-919
• Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian (2016) with Victor Picheny, Stefan Wild and Sebastien Le Digabel. Advances in Neural Information Processing Systems (NIPS) 29, pp. 1435-1443; preprint on arXiv:1605.09466
• Speeding up neighborhood search in local Gaussian process prediction (2016) with Benjamin Haaland; Technometrics, 58(3), pp. 294-303; preprint on arXiv:1409.0074
• laGP: Large-scale spatial modeling via local approximate Gaussian processes in R (2016); Journal of Statistical Software, 72(1), pp. 1-46; provided as a vignette in the laGP package
• Timing Foreign Exchange Markets (2016) with Samuel Malone and Enrique ter Horst; Econometrics, 4(1) 15; preprint available on SSRN:2154035.
• Modeling an augmented Lagrangian for blackbox constrained optimization (2016) with Genetha Gray, Sebastien Le Digabel, Herbie Lee, Pritam Ranjan, Garth Wells and Stefan Wild; Technometrics (with discussion), 58(1), pp. 1-11; preprint on arXiv:1403.4890
• Rejoinder (to Modeling an augmented Lagrangian for blackbox constrained optimization) (2016) with Genetha Gray, Sebastien Le Digabel, Herbie Lee, Pritam Ranjan, Garth Wells and Stefan Wild; Technometrics, 58(1), pp. 26-29
• Hockey player performance via regularized logistic regression (2016) with Matt Taddy and Sen Tian. Chapter in Handbook of Statistical Methods for Design and Analysis in Sports. J. Albert, M. Glickman, R. Koning, and T. Swartz, editors; preprint on arXiv:1510.02172

2015

• Calibrating a large computer experiment simulating radiative shock hydrodynamics (2015) with Derek Bingham, James Paul Holloway, Michael J. Grosskopf, Carolyn C. Kuranz, Erica Rutter, Matt Trantham, R. Paul Drake; Annals of Applied Statistics, 9(3), pp. 1141-1168; preprint on arXiv:1410.3293
• Sequential design for optimal stopping problems (2015) with Mike Ludkovski; SIAM Journal on Financial Mathematics, 6(1), 748-775; preprint on arXiv:1309.3832
• Local Gaussian process approximation for large computer experiments (2015) with Dan Apley; Journal of Computational and Graphical Statistics, 24(2), pp. 561-578; preprint on arXiv:1303.0383
• Efficient Bayesian inference for natural time series using ARFIMA processes (2015) with Tim Graves, Christian Franzke and Nicholas Watkins; Nonlinear Processes in Geophysics, 22, pp. 679-200; preprint on arXiv:1403.2940
• The mesh adaptive direct search algorithm with treed Gaussian process surrogates (2015) with Sebastien Le Digabel; Pacific Journal of Optimization, 11(3), pp. 419-447; Les cahiers du GERAD #G-2011-37; preprint on OO:2011-07-3090

2014

• Exchange rate fundamentals, forecasting, and speculation: Bayesian models in black markets (2014) with Samuel Malone and Enrique ter Horst; Journal of Applied Econometrics, 29(1), pp. 22-41; preprint available here.
• Massively parallel approximate Gaussian process regression (2014) with Jarad Niemi and Robin Weiss; SIAM/ASA Journal on Uncertainty Quantification, 2(1), pp. 564-584; preprint on arXiv:1310.5182
• Market-based credit ratings (2014) with Drew Creal and Ruey Tsay; Journal of Business and Economic Statistics, vol. 32 (3), pp. 430-444; preprint at SSRN:2310260; also see our Chicago Booth Review article.
• Empirical performance modeling of GPU kernels using active learning (2014) with Prasassa Balaprakash, Karl Rupp, Azamat Mametjanov, Paul Hovland and Stefan Wild; ParCo 2013 proceedings in Parallel Computing: Accelerating Computational Science and Engineering (CSE) vol. 25, pp. 646-655; preprint at ANL/MCS-P4097-0713

2013

• Quantifiably secure power grid operation, management, and evolution: A study of uncertainties affecting the grid integration of renewables (2013) with with Genetha Gray, J-P. Watson, and Cesar Silva; technical report SAND2013-7886
• Information-theoretic data discarding for dynamic trees on data streams (2013) with Christoforos Anagnostopoulos; Entropy 15(12), pp. 5510-5535; preprint on arXiv:1201.5568. A short version was presented at the NIPS workshop on Bayesian Optimization, Experimental Design and Bandits (Granada, Spain)
• Bayesian treed response surface models (2013) with Hugh Chipman, Ed George and Rob McCulloch; WIREs Data Mining and Knowledge Discovery, 3(4)
• Active-learning-based surrogate models for empirical performance tuning (2013) with Prasassa Balaprakash and Stefan Wild; in IEEE Cluster 2013 proceedings; preprint at ANL/MCS-P4073-0513
• Bayesian quantile regression for single-index models (2013) with Yuao Hua and Heng Lian; Statistics and Computing, 23(4), 437-454; preprint on arXiv:1110.0219
• Variable selection and sensitivity analysis via dynamic trees with an application to computer code performance tuning (2013) with Matt Taddy and Stefan Wild. Annals of Applied Statistics, 7(1), pp. 51-80; preprint on arXiv:1108.4739; also see our science highlight at Argonne
• Estimating player contribution in hockey with regularized logistic regression (2013) with Shane Jensen, and Matt Taddy. Journal of Quantitative Analysis in Sports, 9(1), pp. 97-111; preprint on arXiv:1209.5026; also see our Chicago Booth Review articles (1) and (2)
• Comment: on advances in expected improvement (2013). An invited discussion of "Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision" by V. Picheny, D. Ginsbourger and G. Caplin. Technometrics, 55(1), pp. 19-20.
• The Importance of Prior Choice in Model Selection: a Density Dependence Example (2013) with James Lawrence, Len Thomas and Stephen Buckland. Methods in Ecology and Evolution, 4(1), pp. 25-33; preprint on arXiv:1108.4912
• Gibbs sampling for ordinary, robust and logistic regression with Laplace priors (2013). Chapter in Bayesian Theory and Applications, honoring Adrian Smith; edited by P. Damien, P. Dellaportas, N.G. Polson and D.A.Stephens; pp 466-482, Oxford University Press

2012

• Regression-based earnings forecasts (2012) with Joseph Gerakos; preprint available on SSRN:2112137; also see our Chicago Booth Review article.
• Gaussian process single-index models as emulators for computer experiments (2012) with Heng Lian; Technometrics, 54(1), pp. 30-41; preprint on arXiv:1009.4241
• Simulation-based regularized logistic regression (2012) with Nicholas Polson; Bayesian Analysis, 7(3), pp. 567-590; preprint on arXiv:1005.3430
• Cases for the nugget in modeling computer experiments (2012) with Herbie Lee. Statistics and Computing, 22(3), pp. 713-722; preprint on arXiv:1007.4580
• Robustness of estimators of long-range dependence and self-similarity under non-Gaussianity (2012) with Christian Franzke, Timothy Graves, Nicholas Watkins, and Cecilia Hughes; Philosophical Transactions of the Royal Society A, 370(1962), pp. 1250-1267; preprint on arXiv:1101.5018

2011

• Dynamic trees for learning and design (2011) with Matt Taddy and Nicholas Polson. Journal of the American Statistical Association, 106(493), pp. 109-123; preprint on arXiv:0912.1586
• Optimization under unknown constraints (2011) with Herbie Lee. Valencia discussion paper, in Bayesian Statistics 9. Oxford University Press; preprint on arXiv:1004.4027
• Optimization subject to hidden constraints via statistical emulation (2011) with Herbie Lee, Crystal Linkletter and Genetha Gray. Pacific Journal of Optimization, 7(3), pp. 467-478; preprint on UCSC-SOE-10-10
• Particle learning of Gaussian process models for sequential design and optimization (2011) with Nicholas Polson. Journal of Computational and Graphical Statistics, 20(1), pp. 102-118; preprint on arXiv:0909.5262
• Classification and categorical inputs with treed Gaussian process models (2011) with Tamara Broderick. Journal of Classification, 28(2), 244-270; preprint on arXiv:0904.4891

2010

• Gaussian Process Structural Equation Models with Latent Variables (2010) with Ricardo Silva. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), P. Grunwald and P. Sprites, editors; preprint on arXiv:1002.4802
• amei: an R package for the Adaptive Management of Epidemiological Interventions (2010) with Dan Merl, Leah Johnson and Marc Mangel. Journal of Statistical Software, 36(6); available as an R vignette in the amei package
• Treed Gaussian processes for classification (2010) with Tamara Broderick. In Hermann Locarek-Junge, Claus Weihs (editors): Classification as a tool for research. Proc. 11th Conference of the International Federation of Classification Societies (IFCS-09), University of Dresden, Germany, March 13-18, 2009. Springer-Verlag, Heidelberg-Berlin, pp. 101-108
• Designing and analyzing a circuit device experiment using treed Gaussian processes (2010) with Herbert K.H. Lee, Matt Taddy and Genetha A. Gray. Chapter in the Handbook of Applied Bayesian Analysis, Anthony O'Hagan and Mike West, editors; Oxford University Press
• Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing (2010) with Ester Pantaleo. Bayesian Analysis 5(2), pp. 237-262; preprint on arXiv:0907.2135
• Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models (2010) with Matt Taddy. Journal of Statistical Software, 33(6); snapshot of one of two R vignettes in the tgp package as of January 2010
• Importance tempering (2010) with Richard Samworth, and Ruth King. Statistics and Computing 20(1), pp. 1-7; preprint on arXiv:0707.4242

2009

• Book review: Ecological Models and Data in R by Benjamin Bolker (2009). The American Statistician, August, 63 (3), pp. 281-282
• tgp: an R package for nonlinear regression by treed Gaussian processes (2009). ISBA Bulletin, Software Spotlight; September 16(3).
• A statistical framework for the adaptive management of epidemiological interventions (2009) with Dan Merl, Leah Johnson and Marc Mangel. PLoS ONE 4(6): e5087
• Adaptive design and analysis of supercomputer experiments (2009) with Herbert K.H. Lee. Technometrics, 51(2), pp. 130-145; preprint on arXiv:0805.4359
• MCMC methods for Bayesian mixtures of copulas (2009) with Ricardo Silva. In D. van Dyk and M. Welling (Eds.), proceedings of the Twelfth International Conference on Artificial Intellegence and Statistics (AISTATS), Clearwater Beach, Florida, April 16-18. JMLR: W&CP 5:512-519
• LogConcDEAD: An R Package for Maximum Likelihood Estimation of a Multivariate Log-Concave Density (2009) with Madeleine Cule and Richard Samworth. Journal of Statistical Software, 29(2); snapshot of the R vignette for the LogConcDEAD package as of January 2009

2008 - 2007

• Gaussian Processes and Limiting Linear Models (2008) with Herbert K.H. Lee. Computational Statistics and Data Analysis, 53, pp. 123-136; preprint on arXiv:0804.4685 (full version of JSM06)
• On estimating covariances between many assets with histories of highly variable length (2008) with Joo Hee Lee and Ricardo Silva; preprint on arXiv:0710.5837
• Bayesian treed Gaussian process models with an application to computer modeling (2008) with Herbert K.H. Lee. Journal of the American Statistical Association, 103(483), pp. 1119-1130; preprint on arXiv:0710.4536
• tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models (2007). Journal of Statistical Software, 19(9); snapshot of the R vignette for the tgp package as of June 2007

2006 - 2005

• Pattern search optimization with a treed Gaussian process oracle (2006). Proceedings of the 14th NECDC, with G.A. Gray, M. Martinez-Canales, M.A. Taddy and H.K.H. Lee; available as Sandia techincal report: SAND2006-794C.
• Gaussian Processes and Limiting Linear Models (2006) with Herbert K.H. Lee. Proceedings of the American Statistical Association, Section on Bayesian Statistical Science, Seattle, WA
• Bayesian treed Gaussian process models (2005). Ph.D. Thesis. Department of Applied Math & Statistics, UC Santa Cruz; winner of the Savage Award for 2006
• Adaptive exploration of computer experiment parameter spaces (2005). ISBA Bulletin, Applications; December 11(4), pp. 3-6; an extended version of this paper, highlighting computation and implementation details, was one of four winners of the ASA (American Statistical Association) Section on Statistical Computing and Graphics student paper competition

2004 - 2003

• Parameter space exploration with Gaussian process trees (2004) with Herbert K. H. Lee and William G. Macready. Proceedings of the International Conference on Machine Learning (ICML) 353-360; Omnipress and ACM Digital Library
• Adaptive Caching by Experts (2003). Masters Thesis. Department of Computer Science, Baskin Engineering School, UC Santa Cruz; also avaliable at the UCSC Science Library.
• Adaptive Caching by Refetching (2003) with Manfred Warmuth, Scott Brandt, and Ismail Ari. Advances in Neural Information Processing Systems (NIPS) 15; pp. 1465-1472; MIT Press

2002 - 2001

• ACME: Adaptive Caching Using Multiple Experts (2002) with Ismail Ari, Ahmed Amer, Ethan Miller, Scott Brandt and Darrell Long; Proceedings in Informatics, vol. 14, pp. 142-158; Carelton Scientific
• Automatic Layout Based Verification of Electrostatic Discharge Paths (2001) with P. Ngan, D. Oliver, T. Smedes, C-K Wong. ESD/EOS Symposium; Portland, OR, USA; p96
• Computer Science Honors Thesis: Shortest Paths and Network Flow Algorithms for Electrostatic Discharge Analysis (2001)
• Math Senior Seminar: Combinatorial Optimization: Matchings (2001)