Grants per year
Personal profile
Research Interests
The interface of simulation models and the real world: uncertainty quantification, model calibration, large-scale simulation, design and analysis of simulation experiments. Methodological interests in all areas of statistical and machine learning.
Education/Academic qualification
Industrial Engineering, PhD, Georgia Institute of Technology
Statistics, MS, Georgia Institute of Technology
Mechanical Engineering, BS, Purdue University
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Dive into the research topics where Matthew Plumlee is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Frameworks: Bayesian Analysis for Nuclei in Diverse Theories
Ohio University, National Science Foundation
7/1/20 → 6/30/25
Project: Research project
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Coastal Probabilistic Hazard Analysis - Year 2
University of North Carolina at Chapel Hill, Federal Emergency Management Agency
9/30/20 → 9/29/21
Project: Research project
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Matthew Plumlee-Argonne joint appointment funding summer 2020
UChicago Argonne, LLC, Argonne National Laboratory, Department of Energy
8/1/20 → 9/15/20
Project: Research project
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libEnsemble library Exascale Computing Project’s (ECP’s) PETSc project
UChicago Argonne, LLC, Argonne National Laboratory, Department of Energy
1/1/20 → 9/30/20
Project: Research project
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Coastal Probabilistic Hazard Analysis
University of North Carolina at Chapel Hill, Federal Emergency Management Agency
9/30/19 → 9/29/21
Project: Research project
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Constructing a Simulation Surrogate with Partially Observed Output
Chan, M. Y. H., Plumlee, M. & Wild, S. M., 2023, (Accepted/In press) In: Technometrics.Research output: Contribution to journal › Article › peer-review
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Inferring sources of substandard and falsified products in pharmaceutical supply chains
Wickett, E., Plumlee, M., Smilowitz, K., Phanouvong, S. & Pribluda, V., 2023, (Accepted/In press) In: IISE Transactions.Research output: Contribution to journal › Article › peer-review
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A Classification Method for Ranking and Selection with Covariates
Keslin, G., Nelson, B. L., Plumlee, M., Pagnoncelli, B. K. & Rahimian, H., 2022, Proceedings of the 2022 Winter Simulation Conference, WSC 2022. Feng, B., Pedrielli, G., Peng, Y., Shashaani, S., Song, E., Corlu, C. G., Lee, L. H., Chew, E. P., Roeder, T. & Lendermann, P. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 156-167 12 p. (Proceedings - Winter Simulation Conference; vol. 2022-December).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces
Eckman, D. J., Plumlee, M. & Nelson, B. L., Nov 2022, In: Operations Research. 70, 6, p. 3473-3489 17 p.Research output: Contribution to journal › Article › peer-review
1 Scopus citations -
Towards precise and accurate calculations of neutrinoless double-beta decay
Cirigliano, V., Davoudi, Z., Engel, J., Furnstahl, R. J., Hagen, G., Heinz, U., Hergert, H., Horoi, M., Johnson, C. W., Lovato, A., Mereghetti, E., Nazarewicz, W., Nicholson, A., Papenbrock, T., Pastore, S., Plumlee, M., Phillips, D. R., Shanahan, P. E., Stroberg, S. R., Viens, F., & 3 others, Dec 2022, In: Journal of Physics G: Nuclear and Particle Physics. 49, 12, 120502.Research output: Contribution to journal › Article › peer-review
Open Access8 Scopus citations
Datasets
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Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
Sung, C. (Contributor), Haaland, B. (Creator), Wang, W. (Contributor) & Plumlee, M. (Creator), Taylor & Francis, 2019
DOI: 10.6084/m9.figshare.7869362.v1, https://tandf.figshare.com/articles/dataset/Multi-Resolution_Functional_ANOVA_for_Large-Scale_Many-Input_Computer_Experiments/7869362/1
Dataset
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Multiresolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
Sung, C. (Creator), Wang, W. (Creator), Plumlee, M. (Creator) & Haaland, B. (Creator), Taylor & Francis, 2021
DOI: 10.6084/m9.figshare.7869362.v3, https://tandf.figshare.com/articles/dataset/Multi-Resolution_Functional_ANOVA_for_Large-Scale_Many-Input_Computer_Experiments/7869362/3
Dataset