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Ross Kunz, Ph.D.


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Dr. M. Ross Kunz is statistician for Idaho National Laboratory developing high-dimensional data visualization in 2D/3D environments and explainable AI techniques. His explainable AI work focuses on the fusion of machine learning and physics applied to a variety of tasks including chemical kinetics, nuclear process control, geology and electric vehicles. He has developed a 3-D visualization framework that allows emergency planners to simulate responses to various safety and security scenarios. His visualization has been presented at the White House and is now being used by federal, state and municipal leaders to plan for expanded use of electric vehicles. He holds a PhD in statistics from Florida State University and a bachelor’s in statistics from Idaho State University. Before joining INL in January 2015 he was a statistician for Michelin of North America.


​Ph.D., Statistics- Flordia State University

M.S., Statistics - Florida State University

B.S., Statistics - Idaho State University


​Kunz, M. R., Borders, T., Redekop, E., Yablonsky, G. S., Constales, D., Wang, L., and Fushimi, R. (2018). Pulse response analysis using the y-procedure: A data science approach. Chemical Engineering Science, 192:46–60.

Kunz, M. R., Kalivas, J. H., and Andries, E. (2010a). Model updating for spectral calibration maintenance and transfer using 1-norm variants of tikhonov regularization. Analytical chemistry,82(9):3642–3649.

Kunz, M. R., Ottaway, J., Kalivas, J. H., and Andries, E. (2010b). Impact of standardization sample design on tikhonov regularization variants for spectroscopic calibration maintenance and transfer. Journal of Chemometrics, 24(3-4):218–229.

Kunz, M. R., Ottaway, J., Kalivas, J. H., Georgiou, C. A., and Mousdis, G. A. (2011). Updating asynchronous fluorescence spectroscopic virgin olive oil adulteration calibration to a new geographical region. Journal of agricultural and food chemistry, 59(4):1051–1057.

Kunz, M. R. and She, Y. (2013). Multivariate calibration maintenance and transfer through robust fused lasso. Journal of Chemometrics, 27(9):233–242.

Medford, A. J., Kunz, M. R., Ewing, S. M., Borders, T., and Fushimi, R. (2018). Extracting knowledge from data through catalysis informatics. ACS Catalysis , 8(8):7403–7429.

Wang, Y., Kunz, M. R., Siebers, S., Rollins, H., Gleaves, J., Yablonsky, G., and Fushimi, R. (2019). Transient kinetic experiments within the high conversion domain: The case of ammonia decomposition. Catalysts, 9(1):104.


Zhang, She and Kunz, Sparse Generalized PCA for Selectable High-Dimensional Analysis, " Joint Statistical Meeting, 2015.

Kunz et al., Multivariate Calibration Maintenance and Transfer through Robust Fused Lasso," J. of Chemom., 2013.

Kunz et al., Updating a Synchronous Fluorescence Spectroscopic Virgin Olive Oil Adulteration Calibration to a new Geographical Region," J. of Agric. Food Chem., 2011.

Kunz et al., Model Updating for Spectral Calibration Maintenance and Transfer Using 1􀀀Norm Variants of Tikhonov Regularization," Ann. Chem., 2010.

Kunz et al., Impact of Standardization Sample Design on Tikhonov Regularization Variants for Spectroscopic Calibration Maintenance and Transfer," J. of Chemom., 2010.

Version: 7.0
Created at 12/18/2017 10:58 AM by Phyllis L. King
Last modified at 4/16/2019 3:42 PM by