• 49 Citations
20092019
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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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Stochastic Simulation Mathematics
Gaussian Process Mathematics
Kriging Mathematics
Discrepancy Mathematics
Stochastic Model Mathematics
Output Mathematics
Gaussian Model Mathematics
Prediction Mathematics

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Grants 2017 2018

Knowledge engineering
Quality control
3D printers
Data transfer
Product design

Research Output 2009 2019

Gradient based criteria for sequential design

Erickson, C. B., Ankenman, B. E., Plumlee, M. & Sanchez, S. M., Jan 31 2019, WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., p. 467-478 12 p. 8632546. (Proceedings - Winter Simulation Conference; vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sequential Design
Response Surface
Simulation Experiment
Entire
Gradient

Plausible optima

Plumlee, M. & Nelson, B. L., Jan 31 2019, WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., p. 1981-1992 12 p. 8632297. (Proceedings - Winter Simulation Conference; vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Function Space
Confidence
Screening
Lipschitz
Optimality

Data-driven parameter calibration in wake models

Liu, B., Byon, E. & Plumlee, M., Jan 1 2018, Wind Energy Symposium. 210029 ed. American Institute of Aeronautics and Astronautics Inc, AIAA, (Wind Energy Symposium, 2018; no. 210029).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Calibration
Wind turbines
Wind power
Power generation
Physics

Improving prediction from stochastic simulation via model discrepancy learning

Lam, H., Zhang, X. & Plumlee, M., Jan 4 2018, 2017 Winter Simulation Conference, WSC 2017. Chan, V. (ed.). Institute of Electrical and Electronics Engineers Inc., p. 1808-1819 12 p. (Proceedings - Winter Simulation Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Stochastic Simulation
Discrepancy
Stochastic Model
Simulation Model
Prediction
1 Citation (Scopus)

Orthogonal Gaussian process models

Plumlee, M. & Roshan Joseph, V., Apr 1 2018, In : Statistica Sinica. 28, 2, p. 601-619 19 p.

Research output: Contribution to journalEditorial

Gaussian Model
Gaussian Process
Process Model
Parametric Modeling
Polynomial Model