Researcher

Andrea Mack

Statistician

Research Areas:

Biography:

Andrea Mack has been a Statistician at Idaho National Laboratory (INL) for 3 years. Prior to INL, she was the Assistant Director of the Statistical Consulting Service at Montana State University and interned at the National Renewable Energy Laboratory. She holds a M.S. in Statistics and a B.S. in Mathematics, both from Montana State University.

At INL Andrea is revising the ASME & ASTM codes for graphite qualification in nuclear reactors using Weibull analysis for the AGR program. Additionally, she works for the NRC, providing Bayesian component failure analysis and trending. At INL Andrea has developed machine learning methods for structural health monitoring of pipes using sensor vibration data, performed uncertainty quantification for qualification of TRISO fuel particles, performed detection limit and sample size calculations, with error propagation for various isotopes, builds and interprets hierarchical models for human factors data, and is developing machine learning methods with graph analysis for Homeland Security. She is currently developing machine learning capabilities for material identification using x-radiology and computed tomography images. Prior to INL, Andrea's work focused in prediction using Bayesian spatial models.

Digital Profiles:

Education:

M.S., Statistics - Montana State University

B.S., Mathematics - Montana State University

Affiliations:

American Statistical Association
National Educators’ Association

Publications:

Manjunatha, K.A., Mack, A.L., Agarwal, V., Koester, D., & Adams, D. 2021. Total unwrapped phase based diagnosis of corrosion process in nuclear power plants secondary piping structures, Structural Health Monitoring. Paper submitted, pending acceptance.

Manjunatha, K.A., Mack, A.L., Agarwal, V., Koester, D., & Adams, D. 2020. Diagnosis of corrosion process in nuclear power plant secondary piping structures, Idaho National Laboratory, U.S. Department of Energy Office of Nuclear Energy Contract DE-NE0008255.

Nuclear Materials Discovery and Qualification Initiative. March, 2020. INL/EXT-20-57732, Revision 0.

Mack, A., Characterization of AGR-1 and AGR-2 UCO TRISO particle layer property distributions, ECAR-5254, October 2020.

Gentillon, C., Atwood, C.L., Mack, A.L., & Ma, Z. 2020. Evaluation of weakly informed priors for FLEX data. INL/EXT-20-58327.

Joe, J.C., Kovesdi, C., Mack, A., and Miyake, T. 2019. The effect of information organization on cognitive workload and visual search performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63, 302-306.

Lammert, M., Bugbee, B., Hou, Y., Mack, A. et al. 2018. Exploring telematics big data for truck platooning opportunities, SAE Technical Paper 2018-01-1083, https://doi.org/10.4271/2018-01-1083.

Plummer, M. and Mack, A., 2018. Graphite characterization: Baseline variability analysis report, Idaho National Laboratory ART Program. https://inldigitallibrary.inl.gov/sites/sti/sti/Sort 5285.pdf

Peterson, D. M., Bowman, J.G.P, Endecott, R.L., Mack, A.L., and Meccage, E.C.G. 2018. The effects of feeding reduced-lignin alfalfa on growing beef cattle performance: a preliminary study, Journal of Agricultural Studies.

Awards:

Exceptional Contributions Program Award, Idaho National Laboratory, 2018

Research Interests:

Weibull analysis, Bayesian analysis, hierarchical models, sample size calculations, experimental design, graph analysis, surrogate modeling, image analysis, machine learning, explainable AI, blockchain technology, cybersecurity, materials science, chemistry, electrical engineering, economics

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