Adam L. Pintar

Statistical Engineering Division 

National Institute of Standards & Technology 

100 Bureau Drive, MS 8980 

Gaithersburg, MD 20899 

Email: adam.pintar@nist.gov 

Oÿce: Bldg. 222-A263 

Phone: (301)975-4554 

Education

• Ph.D. in Statistics 

Iowa State University, Ames, IA, December, 2010 

– Dissertation Title: Model Selection for Good Estimation or Prediction 

Over a User-Specified Covariate Distribution 

• M.S. in Statistics 

Iowa State University, Ames, IA, May, 2007 

– Project Title: Ordering the Works of Plato 

• M.S. in Mathematics 

Pittsburg State University, Pittsburg, KS, December 2004 

– Project Title: Accelerated life Testing Assuming Exponential Distributions 

• B.S. in Mathematics; minor in Computing 

Pittsburg State University, Pittsburg, KS, May, 2003 

1 
Research Interests

• Model Selection 

• Bayesian Methods 

• Experiment Design 

• Statistical Computing 

• Measurement Uncertainty 

• Environmental Statistics 

• Machine Learning 

Professional Experience

• Statistical Engineering Division, National Institute of Standards 

& Technology (NIST), Gaithersburg, MD 

– 2010 - present Mathematical Statistician 

• Department of Statistics, Iowa State University, Ames, IA 

– Spring 08, Fall 08, Spring 09, Fall 09, and Spring 10 Statistical 

Consultant (the Agriculture Experiment Station and the College of 

Engineering) 

• Statistical Sciences Group, Los Alamos National Laboratory, Los 

Alamos, NM 

– Summer 09 Student Intern 

– Summer 08 Student Guest Scholar 

• Statistics in the Community, Ames, IA 

– August 07 - September 10 pro bono statistical consulting 

Honors & Awards

• Department of Commerce Gears of Government Award, NIST, Gaithersburg, MD, 2019, for developing new maps of wind speeds for the US. 

• Department of Commerce Gold Medal, NIST, Gaithersburg, MD, 2018, 

for developing new maps of wind speeds for the US. 

• Materials Measurement Laboratory Accolade for technology transfer, NIST, 

Gaithersburg, MD, 2015. 

• Dan Mowrey Consulting Excellence Award, Department of Statistics, Iowa 

State University, Ames, IA, 2010. 

• Fall Technical Conference Student Grant, Fall Technical Conference, Indianapolis, IN, 2009. 

• Mary G. & Joseph Natrella Scholarship, Quality and Productivity Research 

Conference, Yorktown Heights, NY, 2009. 

2 
• Holly C. & E. Beth Fryer Award in Statistics, Department of Statistics, 

Iowa State University, Ames, IA, 2007. 

• Research Training Group Fellowship, Department of Statistics, Iowa State 

University, Ames, IA, 2007 – 2009. 

• Vertical Integration of Research and Education (VIGRE) Fellowship, Department of Statistics, Iowa State University, Ames, IA, 2006 – 2007. 

• Excellence in Research Award, Department of Mathematics, Pittsburg 

State University, Pittsburg, KS, 2005. 

Publications

Refereed Journal Articles 

• Elliott, L.C., Pintar, A.L., Copeland, C.R., Renegar, T.B., Dixson, R.G., 

Ilic, B.R., Verkouteren, R.M., and Stavis, S.M., (2022) Sub-picoliter Traceability of Microdroplet Gravimetry and Microscopy. Analytical Chemistry, 

94(2) https://doi.org/10.1021/acs.analchem.1c02640. 

• Nandi T.N., Pintar A.L., and Simiu, E., (2022) Influence of Surface 

Roughness Uncertainties on Design of Structures with Open and Suburban 

Exposures. ASCE-ASME Journal of Risk and Uncertainty in Engineering

Systems, Part A: Civil Engineering, 8(1), https://doi.org/10.1061/AJRU 

A6.0001191. 

• Weaver, J.S., Pintar, A.L., Beauchamp, C., Joress, H., Moon, K.W., 

& Phan, T.Q., (2021) Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation 

characterization. Materials & Design, 209, https://doi.org/10.1016/j.ma 

tdes.2021.109969. 

• Chao, Z., Pintar, A.L., Weigand, J., Main, J.A., and Sadek, F., (2021) 

Impact of Variability in Thermal Properties of SFRM on Steel Temperatures in Fire. Fire Safety Journal, 123, https://doi.org/10.1016/j.firesaf.2 

021.103361. 

• Kim, F.H., Pintar, A.L., Obaton, A-F., Fox, J., Tarr, J., and Donmez, A., 

(2021) Merging experiments and computer simulations in X-ray Computed 

Tomography probability of detection analysis of additive manufacturing 

flaws. NDT & E International, 119, 102416 https://doi.org/10.1016/j.nd 

teint.2021.102416. 

• Fox, J.C. and Pintar, A.L., (2021) Prediction of Extreme Value Areal 

Parameters in Laser Powder Bed Fusion of Nickel Superalloy 625. Surface

Topography: Metrology and Properties, 9, https://doi.org/10.1088/2051-

672X/ac0061. 

• Stoudt, S., Pintar, A.L., and Possolo, A., (2021) Coverage Intervals. 

Journal of Research of NIST, 126, https://doi.org/10.6028/jres.126.004. 

• Stoudt, S., Pintar, A.L., and Possolo, A. (2021) Uncertainty Evaluations 

from Small Datasets. Metrologia, 58, https://doi.org/10.1088/1681-

3 
7575/abd372. 

• Jiang, J. Pintar, A.L., Weigand, J.M., Main, J.A., and Sadek, F., (2019) 

Improved calculation method for insulation-based fire resistance of composite slabs. Fire Safety Journal, 105, 144–153, https://doi.org/10.1016/j. 

firesaf.2019.02.013. 

• Kim, F.H., Pintar, A.L., Moylan, S.P., and Garboczi, E.J., (2019) The 

Influence of X-ray Computed Tomography Acquisition Parameters on 

Image Quality and Probability of Detection of Additive Manufacturing 

Defects. Journal of Manufacturing Science and Engineering, 141(11), 

https://doi.org/10.1115/1.4044515. 

• Levine, Z.H., Blattner, T.J., Peskin, A.P., and Pintar, A.L., (2019) 

Scatter corrections in x-ray computed tomography: a physics-based analysis. 

J. Res. NIST, 124, https://doi.org/10.6028/jres.124.013. 

• Duthinh, D., Pintar, A.L., and Simiu, E., (2018) Influence of wind tunnel 

test duration on wind load factors. ASCE Journal of Structural Engineering, 

44(11), https://doi.org/10.1061/(ASCE)ST.1943-541X.0002202. 

• Levine, Z.H., Streater, R.H., Lieberson, A-M.R., Pintar, A.L., Cooksey, 

C.C., and Lemaillet, P., (2017) Algorithm for Rapid Determination of 

Optical Scattering Parameters. Optics Express, 25, 26728 – 26746, https: 

//doi.org/10.1364/OE.25.026728. 

• Streater, R.H., Lieberson, A-M.R., Pintar, A.L., and Levine, Z.H., (2017) 

MCMLpar and MCSLinv: A Parallel Version of MCML and Inverse Monte 

Carlo Algorithm to Calculate Optical Scattering Parameters. Journal

of Research of the National Institute of Standards and Technology, 122 

https://doi.org/10.6028/jres.122.038. 

• Levine, Z.H., Chen-Mayer, H.H., Peskin, A.P., and Pintar, A.L. (2017) 

Comparison of One-Dimensional and Volumetric Computed Tomography 

Measurements of Injected-Water Phantoms. Journal of Research NIST, 

122, https://doi.org/10.6028/jres.122.036. 

• Duthinh, D. Pintar, A.L., Simiu, E., (2017) Estimating Peaks of Stationary Random Processes: A Peaks-over-threshold Approach. ASCE-ASME

Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil

Engineering, 3(4), https://doi.org/10.1016/j.firesaf.2021.103361. 

• Possolo, A. and Pintar, A.L., (2017) Plurality of Type A Evaluations of 

Uncertainty. Metrologia, 54, 617–632. 

• Pérez, D.L., Baker, P.J., Pintar, A.L., Sun, J., Lin, N.J., and Lin-Gibson, 

S., (2017) Experimental and Statistical Methods to Evaluate Antibacterial 

Activity of a Quaternary Pyridinium Salt on Planktonic, Biofilm-forming, 

and Biofilm States. Biofouling, 33, 222–234. 

• Simiu, E., Pintar, A.L., Duthinh, D. and Yeo D., (2017) Wind Load 

Factors for use in the Wind Tunnel Procedure. ASCE-ASME Journal of

Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 

3(4) https://doi.org/10.1061/AJRUA6.0000910. 

• Schantz, M.M., Cleveland, D., Heckert, N.A., Kucklick, J.R., Leigh, S.D., 

Long, S.E., Lynch, J.M., Murphy, K.E., Olfaz, R., Pintar, A.L., Porter, 

B.J., Rabb, S.A., Vander-Pol, S.S., Wise, S.A., and Zeisler R., (2016) 

4 
Development of Two Fine Particulate Matter Standard Reference Materials 

(<4μm and <10μm) for the Determination of Organic and Inorganic 

Constituents. Journal of Analytical and Bioanalytical Chemistry, 408, 

4257–4266. 

• Levine, Z.H., Pintar, A.L., Dobler, J.T., Blume, N., Braun, M., Zaccheo, 

T.S., and Pernini, T.G., (2016) The Detection of Carbon Dioxide Leaks 

using Quasi-tomographic Laser Absorption Spectroscopy Measurements 

in Variable Wind. Atmospheric Measurement Techniques, 9, 1627–1636 

https://www.atmos-meas-tech.net/9/1627/2016/amt-9-1627-2016.pdf. 

• Levine, Z.H. and Pintar, A.L., (2015) A Fixed-memory Moving, Expanding Window for Obtaining Scatter Corrections in X-Ray CT and Other 

Stochastic Averages. Computer Physics Communications, 196, 455–459. 

• Levine, Z.H., Glebov, B.L., Pintar, A. L., and Migdall, A.L., (2015) 

Absolute calibration of a variable attenuator using few-photon pulses, Opt.

Express, 23, 16372 – 16382. 

• Zhang, N.F., and Pintar, A.L., (2015) Monitoring Process Variability for 

Stationary Process Data. Quality and Reliability Engineering International, 

31(8), 1383–1396, https://doi.org/10.1002/qre.1672. 

• Schantz, M.M., Eppe, G., Focant, J-F., Hamilton, C., Heckert, N.A. 

Heltsley, R.M., Hoover, D., Keller, J.M., Leigh, S.D., Patterson Jr., D.G., 

Pintar, A.L., Sharpless, K.E., Sjödin, A., Turner, W.E., Vander-Pol, 

S.S., and Wise, S. A., (2013) Milk and serum standard reference materials 

for monitoring organic contaminants in human samples. Analytical and

bioanalytical chemistry, 405, 1203 – 1211. 

• Pintar, A.L., Anderson-Cook, C.M., Wu, H., (2013) Prediction-based 

Model Selection for Bayesian Multiple Regression Models. Advances and

Applications in Statistics, 32, 83 – 117. 

• Levine, Z.H., Pintar, A.L., Hagedorn, J.G., Fenimore, C.P., and Heussel, 

C.P., (2012) Uncertainties in RECIST as a measure of volume for lung 

nodules and liver tumors. Medical physics, 39, 2628 – 2637. 

• Pintar, A.L., Anderson-Cook, C.M., and Wu, H., (2012) Model Selection 

for Good Estimation and Prediction over a User-Specified Covariate Distribution for Linear Models under the Frequentist Paradigm. Quality and

Reliability Engineering International, 28, 767 – 782. 

• Robinson, T.J., Pintar, A.L., Anderson-Cook, C.M., and Hamada, M.S., 

(2012) A Bayesian Approach to the Analysis of Split-Plot Combined and 

Product Arrays and Optimization in Robust Parameter Design. Journal

of Quality Technology, 44, 304 – 320. 

• Pintar, A.L., Lu, L., Anderson-Cook, C.M., and Silver, G.L., (2012) 

Bayesian estimation of reliability for batches of high reliability single-use 

parts. Quality Engineering, 24, 473 – 485. 

• White, C.C., Hunston, D.L., Tan, K.T., Filliben, J.J., Pintar, A.L., 

and Schueneman, G., (2012) A Systematic Approach to the Study of 

Accelerated Weathering of Building Joint Sealants. Journal of ASTM

International, 9. 

• Patterson, A.R., Baker, R.B., Madson, D. M., Pintar, A.L., and Opriess5 
nig, T., (2011) Disinfection protocols reduce the amount of porcine circovirus type 2 in contaminated 1: 61 scale model livestock transport 

vehicles. Journal of Swine Health and Production, 19, 156 – 164. 

• Hobbs, J., Fostvedt, L., Pintar, A.L., Rocko ̇, D., Kim, E., and Griÿths, 

R., (2010) Fixed-cost vs. Fixed-risk post-election audits in Iowa. Chance, 

23, 13 – 17. 

Refereed Book Chapters 

• Pintar, A.L., White, C., and Sung, L., (2020) Bayesian Hierarchical 

Models for Service Life Prediction or Polymers in Service Life Prediction of

Polymers and Coatings, eds. C. C. White, M. E. Nichols, and J. E. Pickett, 

William Andrew Publishing, 209-231, https://doi.org/10.1016/B978-0-12-

818367-0.00011-4. 

• Pintar, A.L., White, C.C., Hunston, D., and Filliben, J.J., (2017) Predicting Field Degradation of Sealants Using Accelerated Tests for the NIST 

Solar Sphere in Service Life Prediction of Polymers and Plastics Exposed

to Outdoor Weathering, eds. C. C. White, K. M. White, and J. E. Pickett, 

Elsevier, Cambridge, MA, ISBN 9780323497763. 

• Pintar, A.L., and Lombardo, F.T., (2013) Mapping Return Values of 

Extreme Wind Speeds in Risk Assessment and Evaluation of Predictions, 

eds. Ting, M-L., Gail, M., Pfei ̇er, R., Satten, G., Cai, T., and Gandy, A., 

Springer, New York, NY. 

Technical Reports 

• Weigand, J.M., Sadek, F., Thonstad, T., Marcu, S., Villegas, R., Phan, 

L.T., and Pintar, A.L., (2022) Structural Performance of Nuclear Power 

Plant Concrete Structures A ̇ected by Alkali-Silica Reaction (ASR) Task 

3: Assessing Cyclic Performance of ASR-A ̇ected Concrete Shear Walls. 

NIST Technical Note 2180 https://doi.org/10.6028/NIST.TN.2180. 

• Bose, R., Pintar, A.L., and Simiu, E. (2021) Forecasting the Evolution of 

North Atlantic Hurricanes: A Deep Learning Approach. NIST Technical

Note 2167, https://doi.org/10.6028/NIST.TN.2167. 

• Thonstad, T., Weigand, J.M., Sadek, F., Marcu, S., Barrett, T.J., Lew, 

H.S., Phan, L.T., and Pintar, A.L., (2021) Structural Performance of 

Nuclear PowerPlant Concrete Structures A ̇ected by Alkali-Silica Reaction 

(ASR) Task 2: Assessing Bond and Anchorage of Reinforcing Bars in 

ASR-A ̇ected Concrete. NIST Technical Note 2127 https://doi.org/10.6 

028/NIST.TN.2127. 

• Sadek, F., Thonstad, T., Marcu, S., Weigand, J.M., Barrett, T.J., Lew, 

H.S., Phan, L.T., and Pintar, A.L., (2021) Structural Performance of 

Nuclear PowerPlant Concrete Structures A ̇ected by Alkali-Silica Reaction 

(ASR) Task 1: Assessing In-Situ Mechanical Properties of ASR-A ̇ected 

Concrete. NIST Technical Note 2121 https://doi.org/10.6028/NIST.TN. 

2121 

6 
• Pintar, A.L., Simiu, E., Lombardo, F.T., and Levitan, M., (2015) Maps 

of Non-tornadic Wind Speeds With Specified Mean Recurrence Intervals 

for the Contiguous United States Using a Two-dimensional Poisson Process 

Extreme Value Model and Local Regression. NIST Special Publication 500-

301, http://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.500-

301.pdf 

• Zarr, R.R., and Pintar, A.L., (2012) SRM 1453, ExpandedM. Polystyrene 

Board, for Thermal Conductivity from 281 K to 313 K. NIST Special

Publication 260-175, https://www.nist.gov/sites/default/files/documents 

/srm/SP260-175-2.pdf. 

Unrefereed Book Chapters 

• Lombardo, F.T., Pintar, A.L., Possolo, A., Simiu, E., and Yeo, D., (2013) 

Meteorological Extremes. Encyclopedia of Environmetrics, 4. 

Unrefereed Conference Proceedings 

• Lemaillet, P., Cooksey, C.C., Levine, Z.H., Pintar, A.L., Hwang, J., 

and Allen, D.W., (2016) National Institute of Standards and Technology 

measurement service of the optical properties of biomedical phantoms: 

current status. Proc. SPIE 9700, Design and Quality for Biomedical

Technologies IX, 970002 doi:10.1117/12.2214569. 

• Zhang, N.F., and Pintar, A.L., (2014) SPC Charts for Detecting Shifts 

in Variance with Autocorrelated Data, American Statistical Association, 

2013, Proceedings of the Joint Statistical Meetings, Section on Quality and 

Productivity, 467 - 476. 

• Levine, Z.H., Chen-Mayer, H.H., Pintar, A.L., and Sawyer, D.S., (2013) 

Standard Reference Materials for Medical CT. Imaging and Applied Optics, OSA Technical Digest (online) (Optical Society of America), paper 

QW1G.3. http://www.opticsinfobase.org/abstract.cfm?URI=QMI-2013-

QW1G.3. 

• Pintar, A.L., Possolo, A., and Zhang, N.F., (2013) Statistical Methods 

for Change-point Detection in Surface Temperature Records. TEMPERATURE: ITS MEASUREMENT AND CONTROL IN SCIENCE AND

INDUSTRY, VOLUME 8: Proceedings of the Ninth International Temperature Symposium, 1552, AIP Publishing. 

• Pintar, A.L., Possolo, A., and Zhang, N.F., (2013) Regional Homogenization of Surface Temperature Records Using Robust Statistical Methods. 

TEMPERATURE: ITS MEASUREMENT AND CONTROL IN SCIENCE

AND INDUSTRY, VOLUME 8: Proceedings of the Ninth International

Temperature Symposium, 1552, AIP Publishing. 

• Pintar, A.L. and Jayawardhana, A.A., (2006) A Simulation Study to 

Test the Accuracy of Using Maximum Likelihood Predictive Density in 

Quality Control Assuming the Power Rule Model and the Exponential 

Distribution, American Statistical Association, 2005, Proceedings of the 

7 
Joint Statistical Meetings, Section on Quality and Productivity, 1848 -

1853. 

Statistics Digest: The Newsletter of the ASQ Statistics Division 

• Pintar, A.L., (Feb. 2016) Message from the Past Chair of the ASQ 

Statistics Division: Bias Corrected and Accelerated Bootstrap Confidence 

Intervals. 

• Pintar, A.L., (Oct. 2015) Message from the Chair of the ASQ Statistics 

Division. 

• Pintar, A.L., (June 2015) Message from the Chair of the ASQ Statistics 

Division. 

• Pintar, A.L., (Feb. 2015) Message from the Chair of the ASQ Statistics 

Division. 

Blog Posts 

• Pintar, A.L., One Statistical Paradigm to Rule Them All?, https://ww 

w.nist.gov/blogs/taking-measure/one-statistical-paradigm-rule-them-all 

Presentations

Invited 

• Pintar, A.L., Statistical Engineering for Service Life Prediction of Polymers, 2020 DATAWorks conference, virtual, May 1, 2020. 

• Pintar, A.L., Threshold Averaging With Peaks Over Threshold Extreme 

Value Models for Wind Tunnel Tests, Mathematics Department seminar, 

University of Maryland, College Park, MD, November 14, 2019. 

• Pintar, A.L. White, C.C, Hunston, D.L., and Filliben, J.J, Hierarchical 

Bayesian Models for Sealant Data from the NIST Solar Sphere, Service 

Life Prediction of Polymeric Materials: Reaching New Heights, Boulder, 

CO, March 22, 2018. 

• Pintar, A.L., Statistical Model Uncertainty in Measurements: Three 

Examples, Fall Technical Conference, Philadelphia, PA, October 5, 2017. 

• Pintar, A.L., Duthinh, D., and Simiu, E., Estimating the Distribution 

of an Extremum Using a Peaks-over-threshold Model and Monte Carlo 

Simulation, 2017 Test Science Workshop, Springfield, VA, April 5, 2017. 

• Pintar, A.L., Duthinh, D., and Simiu, E., Estimating the Distribution 

of the Peak Pressure Exerted on a Scale Model of a Structure in a Wind 

Tunnel, 2016 Fall Technical Conference, Minneapolis, MN, October 6, 2016. 

• Pintar, A.L. Statistics at the National Institute of Standards and Technology, Second Congreso Internacional de Calidad y Estadistica Aplicada 

(CICEA-II), Lima, Peru, July 22, 2016. 

8 
• Pintar, A.L. Bayesian Adaptive Design for Conformance Testing with 

Bernoulli Trials, Rigorous Test and Evaluation for Defense, Aerospace, and 

National Security Workshop on, Crystal City, VA, April 13, 2016. 

• Pintar, A.L. White, C., and Hunston, D., Predicting Field Degradation 

of Sealants Using Accelerated Tests from the NIST Solar Sphere, Service 

Live Prediction of Polymeric Materials Conference, Santa Fe, NM, March 

24, 2016. 

• Pintar, A.L., Lopez-Pérez, D., and Lin, N., Estimating Properties of 

Antimicrobial Agents Using Statistical Techniques for Data that are Interval 

Censored and Correlated, 2015 Fall Technical Conference, Houston, TX, 

October 8, 2015. 

• Pintar, A.L. and Simiu, E., Development of Extreme Non-hurricane 

Wind Speed Maps for Structural Design from Meteorological Station Measurements in the Contiguous United States, 2014 Fall Technical Conference, 

Richmond, VA, October 3, 2014. 

• Pintar, A.L. and Simiu, E., A Two Stage Approach to Mapping Extreme 

Non-hurricane Wind Speed over the Contiguous United States, European 

network of business and industrial statisticians conference, Linz, Austria, 

September 23, 2014. 

• Pintar, A.L. and Wang, D., Errors in Variables and Ridge Regression 

for Structural Data from Synthesized Amorphous Calcium Phosphate, the 

Joint Research Conference, Seattle, WA, June 26, 2014. 

• Pintar, A.L., Toman, B., Leber, D., and Guthrie, W., Bayesian Adaptive 

Designs for Testing System E ̇ectiveness, at the Quality and Productivity 

Research Conference, Niskayuna, NY, June 5, 2013. 

• Pintar, A.L., Robinson, T.J., Anderson-Cook, C.M., and Hamada, M.S., 

A Bayesian Approach to the Analysis of Split-Plot Product Arrays and 

Prediction in Robust Parameter Design, 2012 Joint Statistical Meetings, 

San Diego, CA, August 1, 2012. 

• Pintar, A.L., Possolo, A., and Zhang, N.F., Statistical Methods for 

Change-Point Detection in Surface Temperature Records, International 

Temperature Symposium 9, Anaheim, CA, March 21, 2012. 

• Pintar, A.L., Mapping Return Values of Extreme Wind Gusts, the 

Conference on Risk Assessment, College Park, MD, October 13, 2011. 

• Pintar, A.L., Anderson-Cook, C.M., and Wu, H., Bayesian Model Selection for Prediction of Future Reliability Using a Generalized Linear Model, 

the Quality and Productivity Research Conference, Roanoke, VA, June, 

2011. 

• Pintar, A.L., Anderson-Cook, C.M., and Wu, H., Prediction Emphasized 

Covariate Selection for Generalized Linear Models, 2009 Fall Technical 

Conference, Indianapolis, IN, October, 2009. 

• Pintar, A.L., Anderson-Cook, C.M., and Wu, H., Mean Squared Error 

in Model Selection, the Quality and Productivity Research Conference, 

Yorktown Heights, NY, June, 2009. 

9 
Discussion 

• Pintar, A.L. The Need and Methods for Routine Inclusion of Model Uncertainty in Statistical Results, 2017 Joint Statistical Meetings, Baltimore, 

MD, July, 2017. 

NIST Seminar 

• Pintar, A.L., Threshold Averaging With Peaks Over Threshold Extreme 

Value Models for Wind Tunnel Tests, Gaithersburg, MD, March 11, 2020. 

• Pintar, A.L., Hierarchical Bayesian Models for Accelerated Degradation 

Tests of Polymers, Gaithersburg, MD, September 20, 2019. 

• Pintar, A.L., Maps of Extreme Wind Speeds, Gaithersburg, MD, April, 

2014. 

• Pintar, A.L. and Toman, B., An R Package for Bayesian Group Sequential 

Design for Binomial Experiments, during Gaithersburg, MD, May, 2013. 

Contributed Presentations 

• Pintar, A.L., Anderson-Cook, C.M., and Wu, H., Selecting Linear Models 

Under the Bayesian Paradigm With Focus on Good Prediction Over a 

User-Specified Distribution on the Covariate Space, 2011 Joint Statistical 

Meetings, Miami Beach, FL, August, 2011. 

• Pintar, A.L., Anderson-Cook, C.M., and Wu, H., Prediction-Based Model 

Selection, 2009 Joint Statistical Meetings, Washington DC, August, 2009. 

• Pintar, A.L. and Maitra, R., Ordering the Works of Plato, 2007 Spring 

Research Conference Ames, IA, May, 2007. 

• Pintar, A.L. and Jayawardhana, A.A., A Simulation Study to Test the 

Accuracy of Using Maximum Likelihood Predictive Density in Quality 

Control Assuming the Power Rule Model and the Exponential Distribution, 

2005 Joint Statistical Meetings, Minneapolis, MN, August, 2005. 

Contributed Posters 

• Pintar, A.L., Levine, Z.H., Yoon, H.W., and Maxwell, S.E., Linearity 

Characterization and Uncertainty Quantification for Spectroradiometers, 

ITL Science Day, virtual, October 28, 2021. 

• Pintar, A.L., Threshold Averaging for Peaks-Over-Threshold Extreme 

Value Analysis of Wind Tunnel Data, ITL Science Day, virtual, October 

29, 2020. 

• Pintar, A.L., Threshold Averaging for Peaks-Over-Threshold Extreme 

Value Analysis of Wind Tunnel Data, 2020 Joint Statistical Meetings, 

virtual, August 4, 2020. 

• Pintar, A.L., White, C., and Sung, L., Bayesian Hierarchical Models for 

Accelerated Degradation Tests of Polymers, ITL Science Day, Gaithersburg, 

MD, November 6, 2019. 

10 
• Pintar, A.L., Jiang, J., Sadek, F., Main, J., and Weigand, J., Improved 

Calculation of Fire Resistance for Composite Concrete Slabs with Sequential Experimentation, Quality and Productivity Research Conference, 

American University, Washington, DC, June 11, 2019. 

• Blattner, T., Garboczi, E., Holmgren, A., Levine, Z., Peskin, A., Pintar, 

A.L., Scatter Corrections in X-ray Computed Tomography: Fundamental Limits and Rapid Algorithms, ITL Science Day, Gaithersburg, MD, 

November 1, 2018. 

• Pintar, A.L., Incorporating Statistical Model Uncertainty by Continuous 

Model Expansion, ITL Science Day, Gaithersburg, MD, November 2, 2017. 

• Pintar, A.L., Pérez, D.L., Lin, N.J., and Lin-Gibson, S., Statistical Methods for Quantifying Antimicrobial Properties of Quaternary Pyridinium 

Salts, ITL Science Day, Gaithersburg, MD, 2016. 

• Pintar, A.L. and Lombardo, F.T., Mapping Return Values of Extreme 

Wind Speeds, ITL Science Day, Gaithersburg, MD, 2012. 

Webinars 

• Pintar, A.L., An Introduction to ANOVA, ASQ Statistics Division, January 16, 2018. 

• Pintar, A.L., ANOVA as Model Comparisons, ASQ Blue Ridge Section, 

May 16, 

2018. 

Standard Reference Materials

Certification of the chemical properties and physical characteristics of Standard Reference Materials (SRMs) to be used for measurement assurance and 

calibration is an important part of NIST’s mission. The certified or reference 

values included on an SRM certificate establish the oÿcial values of the relevant 

quantities (e.g. the pH or the mass fraction of sulfur in a material) on the basis 

of an extensive and often complex experiment designed and analyzed at NIST 

by a statistician in collaboration with a team of highly-qualified scientists or 

engineers. 

See this website for more information about SRMs http://www.nist.gov/srm/i 

ndex.cfm. 

A NIST Mathematical Statistician must write an accompanying report of analysis 

for each SRM that they work on, for internal use, and to be reviewed and approved 

by the Chief Statistician of the Statistical Engineering Division. 

• SRM C2415a Battery Lead (UNS 52770) 

• SRM 2910b Hydroxyapatite 

• SRM 2787 Fine Particulate Matter (less than 10 micrometers) 

• SRM 2786 Fine Particulate Matter (less than 4 micrometers) 

11 
• SRM 2780a Hard Rock Mine Waste 

• SRM 2779 Gulf of Mexico Crude Oil 

• SRM 2777 Weathered Gulf of Mexico Crude 

• SRM 2718a Green Petroleum Coke 

• SRM 2685c Bituminous Coal (Nominal Mass Fraction 5% Sulfur) 

• SRM 2684c Bituminous Coal (Nominal Mass Fraction 3% Sulfur) 

• SRM 2683c Bituminous Coal (Nominal Mass Fraction 2 % Sulfur) 

• SRM 2682c Subbituminous Coal (Nominal Mass Fraction 0.5% Sulfur) 

• SRM 2454a Hydrogen in Titanium Alloy (Nominal Mass Fraction 215 

mg/kg H) 

• SRM 2453a Hydrogen in Titanium Alloy (Nominal Mass Fraction 125 

mg/kg H) 

• SRM 2452 Hydrogen in Titanium Alloy (Nominal Mass Fraction 60 

mg/kg H) 

• SRM 2088 Density Standard for Medical Computed Tomography 

• SRM 2087 Dimensional Standard for Medical Computed Tomography 

• SRM 1991 Mixed Coal Tar/Petroleum Extract in Methylene Chloride 

• SRM 1974c Organics in Mussel Tissue (Mytilus edulis) 

• SRM 1957 Organic Contaminants in Non-Fortified Human Serum 

• SRM 1635a Trace Elements in Coal (Subbituminous) 

• SRM 1633c Trace Elements in Coal Fly Ash 

• SRM 1632e Trace Elements in Coal (Bituminous) 

• SRM 1584 Priority Pollutant Phenols in Methanol 

• SRM 1582 Petroleum Crude Oil 

• SRM 1580 Organics in Shale Oil 

• SRM 1453 Thermal Conductivity – Expanded Polystyrene Board 

• SRM 1412a Multicomponent Glass 

• SRM 1085c Wear Metals in Lubricating Oil 

• SRM 612 Trace Elements in Glass. 

• SRM 610 Trace Elements in Glass. 

• SRM 131h Refined Cast Iron. 

Teaching and Mentoring

University Courses 

• EMAP 502 co-instructor, Georgetown University (Fall 18, Fall 19, Fall 

20, Fall 21). 

• STAT 543 Probability and Statistics II Teaching Assistant, Iowa 

State University (Spring 09). 

• STAT 542 Theory of Probability and Statistics Teaching Assistant, 

Iowa State University (Fall 07, Fall 08). 

• STAT 104 Introduction to Statistics Course Instructor, Iowa State 

University (Spring 07, Fall 06, Summer 06, Spring 06, Fall 05, Summer 

12 
05). 

• STAT 101 Principles of Statistics Lab Instructor and Grader, Iowa 

State University (Spring 05). 

• MATH 110 College Algebra with Review Course Instructor, Pittsburg State University (Fall 04). 

• MATH 143 Elementary Statistics Course Instructor, Pittsburg State 

University (Spring 04). 

• MATH 113 College Algebra Course Instructor, Pittsburg State University (Fall 03, Spring 04). 

Short Courses 

• Pintar, A.L. and Koepke, A.A., Bayesian Methods for Quantifying Measurement Uncertainty, NIST, Boulder, CO, March 27 – 29, 2018. 

• Pintar, A.L., Toman B., and Rukhin A., Bayesian Methods, NIST, 

Gaithersburg, MD, December 5th, 7th, and 8th, 2016. 

• Pintar, A. L., Applied Problem Solving and Research Using Statistical 

Methods with NIST Examples, September 22, 2016 in Regina, Canada, 

and September 23, 2016 in Saskatoon, Canada. 

• Pintar, A.L. Bayesian Metrology Introduction with NIST Examples, the 

American Society of Mechanical Engineers PTC 19.1 meeting, Savannah, 

GA, January 14, 2016. 

• Pintar, A.L., Toman B., and Rukhin A., Bayesian Methods, NIST, 

Gaithersburg, MD, December 2nd and 3rd, 2015. 

• Pintar, A.L., Toman, B. and Leber, D., Bayesian Methods, NIST, 

Gaithersburg, MD, 2012. 

Tutorials 

• Pintar, A.L., Some Common Experiment Designs, November 4, 2020, for 

a group in the NIST Engineering Laboratory, and on January 6, 2021 for 

a group in the NIST Physical Measurement Laboratory. 

• Pintar, A.L., R Introduction: Lesson Two, NIST, Gaithersburg, MD, 

February 28, 2018. 

• Pintar, A.L., R Introduction: Lesson One, NIST, Gaithersburg, MD, 

February 14 and June 18, 2018. 

Summer Undergraduate Research Fellowship (SURF) 

• K. Sadek (2020) Designing Experiments to Estimate the Non-linear Response of a Spectroradiometer 

• M. Kasner (2016) Simulation Study of the Accuracy of Predictions from 

Poisson Process Extreme Value Models 

• M. Milosavljevic (2014) Seasonal and Spatial Patterns in the Atmospheric 

Concentration of Greenhouse Gases 

13 
Other Mentoring 

• Virginia Tech. Computational Modeling and Data Analytics Capstone 

Project Program, Spring 2021, Spring 2022, contributed a project and 

mentored the team that worked on the project. 

Educational Outreach 

• Coordinate a STEM program for the kindergarten class of the NIST Child 

Care Center. My e ̇ort consists of recruiting NIST scientists willing to 

give laboratory tours and/or lead STEM activities with the kindergarten 

class, and scheduling the tours/activities, 2017 – present. 

• Judge for the high school Mathematics and Physics category of the 2016 

Montgomery County Science Fair held on March 12, 2016. 

• Richard Montgomery High School Career Day, presentation about NIST, 

February, 2016. 

• Howard County Math Festival, interactive presentation on recurrence plots 

of voice recordings, November, 2015 with Steve Lund and Hari Iyer. 

• Howard County Math Festival, presentation on variability by collecting 

reaction time data interactively , November, 2014 with Steve Lund and 

Hari Iyer. 

• Howard County Math Festival, presentation on the Monte Hall problem, 

January, 2014 with Fern Hunt. 

Funded Research Proposals

Building the Future 

• Pintar, A., Haber, G., Choong, Y., Buchanan, K., Analysis of NIST 

Survey Data, FY 2022. 

• Pintar, A. Iyer, H. Lund, S., Rukhin, A. and Guthrie, W., Taming Model 

Uncertainty, FY 2018. 

• Pintar, A.L., Blattner, T.J., Peskin, A.P., and Levine Z.H., Tomography 

with scattering: a likelihood-guided approach, FY 2018. 

Strategic and Emerging Research Initiatives Proposals 

• Madison, A. and Stavis, S., Camp, C., Pintar, A., Nanoplastic Arrays for 

Microspectroscopy Calibrations and Sorption Studies, FY 2021 and 2022. 

External to NIST 

• Herman, M., Lund, S., Iyer, H., Dogan, G., Pintar, A., Quantitative 

Evaluation of Footwear Evidence: Advancing the Footwear Impression 

Comparison System (FICS) towards Casework Application, funded by the 

NIJ, FY 2021 and 2022. 

14 
Professional Service

Editorial 

• Associate Editor, Transactions on Mathematical Software, current. 

• Associate Editor, Statistical Analysis and Data Mining, past. 

• Associate Editor, Journal of Statistics Education, past. 

Review and Referee 

• Applied Stochastic Models in Business and Industry 

• ASTM (Standards) 

• Environmental Science and Technology 

• Journal of Metrology Society of India 

• Journal of Quality Technology 

• Journal of Research of NIST 

• Journal of Statistical Inference and Planning 

• Journal of Statistics Education 

• Journal of Structural Engineering 

• Kansas State Applied Statistics in Agriculture Conference Proceedings 

• Metrologia 

• Polymer Degradation and Stability 

• Precision Engineering 

• Quality Engineering 

• Technometrics 

• Transactions on Mathematical Software 

• Washington Editorial Review Board 

Conference Chair 

• 2019 Fall Technical Conference, NIST, Gaithersburg, MD. 

American Society for Quality 

• Technical Reviewer for the Statistics Digest of the American Society 

for Quality Statistics Division, April, 2022 – present 

• Committee Member, Fall Technical Conference program committee, 

American Society for Quality Statistics Division representative, 2018. 

• Webinar Coordinator of the American Society for Quality Statistics 

Division, January, 2017 – 2019. 

• Past Chair of the American Society for Quality Statistics Division, January, 2016 – December, 2016. 

• Chair of the American Society for Quality Statistics Division, January, 

2015 – December, 2015. 

15 
• Chair Elect of the American Society for Quality Statistics Division, 

January, 2014 – December, 2014. 

• Treasurer of the American Society for Quality Statistics Division, July, 

2011 – December, 2013. 

American Statistical Association 

• Committee Member, W.J. Youden Award in Interlaboratory Testing, 

current. 

• Committee Member, Mary G. and Joseph Natrella Scholarship, current. 

DATAWorks Workshop 

• Committee Member, DATAWorks workshop program committee, 2018. 

Institutional Service 

• Statistical Engineering Division committee tasked with reviewing, documenting, and revising when necessary, the hiring practices of the Statistical 

Engineering Division, ongoing. 

• Seminar coordinator for the Statistical Engineering Division of NIST, 2016 

– present. 

• Backup evacuation coordinator for the Statistical Engineering Division of 

NIST, 2015 – present. 

• Judge for the Sigma-Xi poster contest in 2011 and 2014. The contest is 

held among NIST postdoctoral researchers. 

• Member of the NIST ITL awards committee in 2013. 

• Student member of the Student Programs Advisory Committee at Los 

Alamos National Laboratory, May, 2009 - August, 2009. 

• Student member of the department library committee in the Department 

of Statistics at Iowa State University, 2009 - 2010. 

• Student member of the department social committee in the Department of 

Statistics at Iowa State University, 2008 - 2009. 

• Recycling coordinator for the Iowa STATERS, 2007 - 2008. 

Software

Released 

• R Package: potMax Estimate the Distribution of the Maximum of a 

Time Series Using Peaks-over-threshold Models https://github.com/usnis 

tgov/potMax 

• R Package: mewAvg A Fixed Memory Moving Expanding Window 

Average https://cran.r-project.org/web/packages/mewAvg/index.html 

16 
Developed but not Released 

• R Package: WindMap Maps of Return Values for Extreme Winds Over 

the Contiguous United States 

– This software was developed in conjunction with a project that had 

the goal of proposing maps of extreme wind speeds to revise the maps 

in the 2010 edition of the ASCE/SEI 7 publication. Numeric versions 

of the maps were developed in a separate analysis. The software 

displays and extracts values from the numeric versions 

• R Package: AdaptiveDesign Calculate Bayesian Adaptive Experiment 

Designs 

– This software is for calculating a Bayesian adaptive experiment design 

to test the hypotheses H0: p <= p0 versus Ha: p > p0. The output 

is a tree describing the experiment. To view the tree in a nice form, 

graphviz is also needed. 

Computing Environments and Languages

Substantial Experience 

R, C, Python, Jags, Stan, LaTeX 

Some Experience 

Julia, SAS, JMP, Minitab, Bash, Fortran, C++ 

17