Grants per year
Personal profile
Research Interests
Statistical modeling and analysis of engineering and industrial systems; statistical learning and predictive analytics; quality engineering and six sigma; manufacturing process diagnosis and control.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Mechanical Engineering, PhD, Concentration: Manufacturing Process Modeling, Diagnosis, and Adaptive Control, University of Michigan
… → 1997
Electrical Engineering, MS, Concentration: Signal Processing and Automatic Control, University of Michigan
… → 1995
Mechanical Engineering, MS, Concentration: Manufacturing, University of Michigan
… → 1992
Mechanical Engineering, BS, Concentration: Design and Automatic Control, University of Michigan
… → 1990
Research interests keywords
- Manufacturing process diagnosis and control
- Quality engineering and Qix sigma
- Statistical learning and data mining
- Statistical modeling and analysis of engineering systems
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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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Collaborative Research: Inference on expensive, grey-box simulation models
Nelson, B. L. (PD/PI), Nelson, B. L. (PD/PI), Plumlee, M. (PD/PI), Plumlee, M. (PD/PI), Apley, D. (Co-PD/PI), Apley, D. (Co-PD/PI), Nelson, B. L. (Co-PD/PI) & Nelson, B. L. (Co-PD/PI)
8/1/22 → 7/31/26
Project: Research project
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Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials
Chen, W. (PD/PI), Apley, D. (Co-PD/PI) & Rondinelli, J. M. (Co-PD/PI)
Advanced Research Projects Agency - Energy
4/28/20 → 11/30/22
Project: Research project
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Targeted Healthcare Engineering for Systems Interventions in Stroke (THESIS)
Maas, M. B. (PD/PI), Maas, M. B. (PD/PI), Maas, M. B. (PD/PI), Richards, C. T. (PD/PI), Ankenman, B. E. (Co-Investigator), Apley, D. (Co-Investigator), Maas, M. B. (Co-Investigator) & Perry, O. (Co-Investigator)
The University of Chicago, Agency for Healthcare Research and Quality
9/30/19 → 9/29/24
Project: Research project
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Collaborative Research: Model-Based Multidisciplinary Dynamic Decisions in Design
Apley, D. (PD/PI) & Chen, W. (Co-PD/PI)
9/1/15 → 8/31/19
Project: Research project
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Daniel Apley - Subproject for Institution # SP0032077
Apley, D. (Subproject PI)
8/15/15 → 7/31/20
Project: Research project
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Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data
Zhang, H., Georgescu, A. B., Yerramilli, S., Karpovich, C., Apley, D. W., Olivetti, E. A., Rondinelli, J. M. & Chen, W., 2025, (Accepted/In press) In: Accounts of Materials Research.Research output: Contribution to journal › Article › peer-review
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Evaluating Acute Stroke Diagnosis Using Simulation Scenarios
Liberman, A. L., Apley, D., Zhu, J., Romo, E., Holl, J. L., Khorzad, R., Maas, M. B., Mendelson, S. J., Richards, C. T., Song, S. & Prabhakaran, S., 2025, (Accepted/In press) In: Annals of Emergency Medicine.Research output: Contribution to journal › Article › peer-review
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Uncertainty quantification and propagation for multiscale materials systems with agglomeration and structural anomalies
Comlek, Y., Mojumder, S., van Beek, A., Prabhune, P., Ciampaglia, A., Apley, D. W., Brinson, L. C., Liu, W. K. & Chen, W., Feb 15 2025, In: Computer Methods in Applied Mechanics and Engineering. 435, 117531.Research output: Contribution to journal › Article › peer-review
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Coefficient tree regression: fast, accurate and interpretable predictive modeling
Sürer, Ö., Apley, D. W. & Malthouse, E. C., Jul 2024, In: Machine Learning. 113, 7, p. 4723-4759 37 p.Research output: Contribution to journal › Article › peer-review
2 Scopus citations -
Discovering interpretable structure in longitudinal predictors via coefficient trees
Sürer, Ö., Apley, D. W. & Malthouse, E. C., Dec 2024, In: Advances in Data Analysis and Classification. 18, 4, p. 911-951 41 p.Research output: Contribution to journal › Article › peer-review
Datasets
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Density Deconvolution With Additive Measurement Errors Using Quadratic Programming
Yang, R. (Creator), Apley, D. W. (Creator), Staum, J. (Creator) & Ruppert, D. (Creator), Taylor & Francis, 2021
DOI: 10.6084/m9.figshare.11396871.v3, https://tandf.figshare.com/articles/dataset/Density_Deconvolution_with_Additive_Measurement_Errors_using_Quadratic_Programming_revision_/11396871/3
Dataset
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Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data
Bui, A. T. (Creator) & Apley, D. W. (Creator), Taylor & Francis, 2021
DOI: 10.6084/m9.figshare.13708310, https://tandf.figshare.com/articles/dataset/Analyzing_Nonparametric_Part-to-part_Variation_in_Surface_Point_Cloud_Data/13708310
Dataset
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Density Deconvolution with Additive Measurement Errors using Quadratic Programming (revision)
Yang, R. (Creator), Apley, D. W. (Creator), Staum, J. (Creator) & Ruppert, D. (Creator), Taylor & Francis, 2019
DOI: 10.6084/m9.figshare.11396871.v1, https://tandf.figshare.com/articles/Density_Deconvolution_with_Additive_Measurement_Errors_using_Quadratic_Programming_revision_/11396871/1
Dataset
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A Preposterior Analysis to Predict Identifiability in Experimental Calibration of Computer Models
Arendt, P. D. (Creator), Apley, D. W. (Creator) & Chen, W. (Creator), figshare, 2015
DOI: 10.6084/m9.figshare.1496544.v1, https://figshare.com/articles/A_Preposterior_Analysis_to_Predict_Identifiability_in_Experimental_Calibration_of_Computer_Models/1496544/1
Dataset
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A preposterior analysis to predict identifiability in the experimental calibration of computer models
Arendt, P. D. (Creator), Apley, D. W. (Creator) & Chen, W. (Creator), figshare, 2015
DOI: 10.6084/m9.figshare.1496544, https://figshare.com/articles/A_preposterior_analysis_to_predict_identifiability_in_the_experimental_calibration_of_computer_models/1496544
Dataset