Publications
Topics: all · Gaussian process · tree-based methods · multiscale modeling · hierarchical mixture · space-time process · Bayesian UQ · point process
Underlined names are PhD student advisees.
- 19. Chen, Z. and Ma, P. (2026). Space-time covariance with screening effects, Markov property, and long-range dependence. Preprint.
[bibtex]
@unpublished{Chen2026, author = {Chen, Z. and Ma, P.}, title = {Space-time covariance with screening effects, {Markov} property, and long-range dependence}, year = {2026}, note = {Preprint} } - 18. Ma, P. (2026). Bayesian soft-tree Poisson process model for covariate-dependent point patterns.
[arXiv] [Code] [R package] [bibtex]
@unpublished{Ma2026softtree, author = {Ma, P.}, title = {{Bayesian} soft-tree {Poisson} process model for covariate-dependent point patterns}, year = {2026}, note = {arXiv:2610.11343} } - 17. Ye, C. and Ma, P. (2026). Bayesian Treed Vecchia Gaussian Process Emulation via Sequential Monte Carlo. Preprint.
[bibtex]
@unpublished{Ye2026, author = {Ye, C. and Ma, P.}, title = {{Bayesian Treed Vecchia Gaussian Process Emulation via Sequential Monte Carlo}}, year = {2026}, note = {Preprint} } - 16. Ma, P. and Ma, L. (2026). A residual tree Gaussian process modeling framework for high-dimensional data.
[arXiv] [Code] [R package] [bibtex]
@unpublished{Ma2026restree, author = {Ma, P. and Ma, L.}, title = {A residual tree {Gaussian} process modeling framework for high-dimensional data}, year = {2026}, note = {arXiv:2610.02893} } - 15. Sukianto, T., Kuusela, M., Giglio, D., Mondal, A., Ma, P., and Nychka, D. W. (2026). Locally stationary Argo ocean heat content estimates: modeling, validation and uncertainty quantification.
[arXiv] [bibtex]
@unpublished{Sukianto2026, author = {Sukianto, T. and Kuusela, M. and Giglio, D. and Mondal, A. and Ma, P. and Nychka, D. W.}, title = {Locally stationary {Argo} ocean heat content estimates: modeling, validation and uncertainty quantification}, year = {2026}, note = {arXiv:2606.31957} } - 14. McCrimmon, C. S. and Ma, P. (2026). Multivariate Gaussian process emulation for multifidelity computer models with high-dimensional spatial outputs. Technometrics, accepted.
[arXiv] [Code] [bibtex]
@article{McCrimmon2026, author = {McCrimmon, C. S. and Ma, P.}, title = {Multivariate {Gaussian} process emulation for multifidelity computer models with high-dimensional spatial outputs}, journal = {Technometrics}, year = {2026}, eprint = {2603.27463}, archiveprefix = {arXiv}, note = {Accepted} } - 13. Ma, P. (2026). A sequential Monte Carlo method for Bayesian Poisson process trees. Stat, accepted.
[DOI] [R package] [bibtex]
@article{Ma2026ppt, author = {Ma, P.}, title = {A sequential {Monte Carlo} method for {Bayesian} {Poisson} process trees}, journal = {Stat}, year = {2026}, doi = {10.1002/sta4.70181} } - 12. Ma, P. (2025). Asymmetric space-time covariance functions via hierarchical mixtures.
[arXiv] [Code] [R package] [bibtex]
@unpublished{Ma2025, author = {Ma, P.}, title = {Asymmetric space-time covariance functions via hierarchical mixtures}, year = {2025}, note = {arXiv:2511.07959} } - 11. Ma, P. and Bhadra, A. (2023). Beyond Matérn: on a class of interpretable confluent hypergeometric covariance functions. Journal of the American Statistical Association, 118(543), 2045–2058.
[arXiv] [DOI] [R package] [bibtex]
@article{Ma2023, author = {Ma, P. and Bhadra, A.}, title = {Beyond {Mat\'ern}: on a class of interpretable confluent hypergeometric covariance functions}, journal = {Journal of the American Statistical Association}, year = {2023}, volume = {118}, number = {543}, pages = {2045--2058}, doi = {10.1080/01621459.2022.2027775} } - 10. Ma, P., Karagiannis, G., Konomi, B. A., Asher, T. G., Toro, G. R., and Cox, A. T. (2022). Multifidelity computer model emulation with high-dimensional output: an application to storm surge. Journal of the Royal Statistical Society: Series C, 71(4), 861–883.
[arXiv] [DOI] [Code] [R package] [bibtex]
@article{Ma2022multifidelity, author = {Ma, P. and Karagiannis, G. and Konomi, B. A. and Asher, T. G. and Toro, G. R. and Cox, A. T.}, title = {Multifidelity computer model emulation with high-dimensional output: an application to storm surge}, journal = {Journal of the Royal Statistical Society: Series C}, year = {2022}, volume = {71}, number = {4}, pages = {861--883}, doi = {10.1111/rssc.12558} } - 9. Baker, E., Barbillon, P., Fadikar, A., Gramacy, R. B., Herbei, R., Higdon, D., Huang, J., Johnson, L. R., Ma, P., Mondal, A., Pires, B., Sacks, J., and Sokolov, V. (2022). Analyzing stochastic computer models: a review with opportunities. Statistical Science, 37(1), 64–89.
[arXiv] [DOI] [bibtex]
@article{Baker2022, author = {Baker, E. and Barbillon, P. and Fadikar, A. and Gramacy, R. B. and Herbei, R. and Higdon, D. and Huang, J. and Johnson, L. R. and Ma, P. and Mondal, A. and Pires, B. and Sacks, J. and Sokolov, V.}, title = {Analyzing stochastic computer models: a review with opportunities}, journal = {Statistical Science}, year = {2022}, volume = {37}, number = {1}, pages = {64--89}, doi = {10.1214/21-STS822} } - 8. Ma, P., Mondal, A., Konomi, B. A., Hobbs, J., Song, J. J., and Kang, E. L. (2022). Computer model emulation with high-dimensional functional output in large-scale observing system uncertainty experiments. Technometrics, 64(1), 65–79.
[arXiv] [DOI] [Code] [bibtex]
@article{Ma2022emulation, author = {Ma, P. and Mondal, A. and Konomi, B. A. and Hobbs, J. and Song, J. J. and Kang, E. L.}, title = {Computer model emulation with high-dimensional functional output in large-scale observing system uncertainty experiments}, journal = {Technometrics}, year = {2022}, volume = {64}, number = {1}, pages = {65--79}, doi = {10.1080/00401706.2021.1895890} } - 7. Ma, P. (2020). Objective Bayesian analysis of a cokriging model for hierarchical multifidelity codes. SIAM/ASA Journal on Uncertainty Quantification, 8(4), 1358–1382.
[arXiv] [DOI] [Code] [R package] [bibtex]
@article{Ma2020objective, author = {Ma, P.}, title = {Objective {Bayesian} analysis of a cokriging model for hierarchical multifidelity codes}, journal = {SIAM/ASA Journal on Uncertainty Quantification}, year = {2020}, volume = {8}, number = {4}, pages = {1358--1382}, doi = {10.1137/19M1289893} } - 6. Ma, P. and Kang, E. L. (2020). A fused Gaussian process model for very large spatial data. Journal of Computational and Graphical Statistics, 29(3), 479–489.
[arXiv] [DOI] [Code] [bibtex] Student paper award, ICSA 2017; honorable mention, ASA ENVR 2016
@article{Ma2020fused, author = {Ma, P. and Kang, E. L.}, title = {A fused {Gaussian} process model for very large spatial data}, journal = {Journal of Computational and Graphical Statistics}, year = {2020}, volume = {29}, number = {3}, pages = {479--489}, doi = {10.1080/10618600.2019.1704293} } - 5. Ma, P. and Kang, E. L. (2019). Spatio-temporal data fusion for massive sea surface temperature data from MODIS and AMSR-E instruments. Environmetrics, 31(2), e2594.
[arXiv] [DOI] [bibtex]
@article{Ma2019fusion, author = {Ma, P. and Kang, E. L.}, title = {Spatio-temporal data fusion for massive sea surface temperature data from {MODIS} and {AMSR-E} instruments}, journal = {Environmetrics}, year = {2019}, volume = {31}, number = {2}, pages = {e2594}, doi = {10.1002/env.2594} } - 4. Konomi, B. A., Hanandeh, A. A., Ma, P., and Kang, E. L. (2019). Computationally efficient nonstationary nearest neighbor Gaussian process models using data-driven techniques. Environmetrics, 30(8), e2571.
[DOI] [bibtex]
@article{Konomi2019, author = {Konomi, B. A. and Hanandeh, A. A. and Ma, P. and Kang, E. L.}, title = {Computationally efficient nonstationary nearest neighbor {Gaussian} process models using data-driven techniques}, journal = {Environmetrics}, year = {2019}, volume = {30}, number = {8}, pages = {e2571}, doi = {10.1002/env.2571} } - 3. Ma, P., Konomi, B. A., and Kang, E. L. (2019). An additive approximate Gaussian process model for large spatio-temporal data. Environmetrics, 30(8), e2569.
[arXiv] [DOI] [bibtex]
@article{Ma2019additive, author = {Ma, P. and Konomi, B. A. and Kang, E. L.}, title = {An additive approximate {Gaussian} process model for large spatio-temporal data}, journal = {Environmetrics}, year = {2019}, volume = {30}, number = {8}, pages = {e2569}, doi = {10.1002/env.2569} } - 2. Ma, P., Kang, E. L., Braverman, A., and Nguyen, H. (2019). Spatial statistical downscaling for constructing high-resolution nature runs in global observing system simulation experiments. Technometrics, 61(3), 322–340.
[arXiv] [DOI] [Code] [bibtex] Student paper award, ASA ENVR 2018
@article{Ma2019downscaling, author = {Ma, P. and Kang, E. L. and Braverman, A. and Nguyen, H.}, title = {Spatial statistical downscaling for constructing high-resolution nature runs in global observing system simulation experiments}, journal = {Technometrics}, year = {2019}, volume = {61}, number = {3}, pages = {322--340}, doi = {10.1080/00401706.2018.1524791} } - 1. Cawse-Nicholson, K., Fisher, J. B., Famiglietti, C. A., Braverman, A., Schwandner, F. M., Lewicki, J. L., Townsend, P. A., Schimel, D. S., Pavlick, R., Bormann, K. J., Ferraz, A., Kang, E. L., Ma, P., Bogue, R. R., Youmans, T., and Pieri, D. C. (2018). Ecosystem responses to elevated CO2 using airborne remote sensing at Mammoth Mountain, California. Biogeosciences, 15(24), 7403–7418.
[DOI] [bibtex]
@article{CawseNicholson2018, author = {Cawse-Nicholson, K. and Fisher, J. B. and Famiglietti, C. A. and Braverman, A. and Schwandner, F. M. and Lewicki, J. L. and Townsend, P. A. and Schimel, D. S. and Pavlick, R. and Bormann, K. J. and Ferraz, A. and Kang, E. L. and Ma, P. and Bogue, R. R. and Youmans, T. and Pieri, D. C.}, title = {Ecosystem responses to elevated {CO$_2$} using airborne remote sensing at {Mammoth Mountain}, {California}}, journal = {Biogeosciences}, year = {2018}, volume = {15}, number = {24}, pages = {7403--7418}, doi = {10.5194/bg-15-7403-2018} }
Software
- ResTree (R): residual tree Gaussian process models for high-dimensional spatial data
- poistree (R): Bayesian Poisson point process tree models
- GPBayes (R, CRAN): Gaussian process modeling for uncertainty quantification
- ARCokrig (R, CRAN): autoregressive cokriging for multifidelity computer models
- STVGP (R): space-time covariance models with Vecchia approximation
- gpbayeskit (Python): space-time models for spatio-temporal data
- Spacetime Explorer: interactive visualization of space-time covariance functions