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The Statistical Sciences group at Sandia consists of a mix of M.S. and Ph.D. level statisticians with a wide range of expertise and experience and have built many enduring relationships throughout Sandia over the years. Our main areas of expertise are in design of experiments, sampling and test plans, and sample size calculations, statistical quality control, statistical reliability and maintainability, quantification of margin and uncertainty, and statistical graphics and computing. The individual bios below provide more detail for each staff member.


JSM 2019

Management Team


Adele Doser, Manager

Staff Members


Dan Campbell

Dan Campbell joined the statistics team at Sandia National Laboratories in March of 2014. Dan earned an M.S. in Statistics at Virginia Tech. He also holds a B.S. in Mathematics from the University of Utah. Between grad school and Sandia, Dan worked as a Data Analyst in the Charlotte, NC area for 2 years. Dan strives for excellence in helping others make data informed decisions, and enjoys the diversity of projects and tasks he is involved with at Sandia. He supports several engineering programs through a wide variety of statistical methods such as EDA, data visualization, DOEx, simulation studies, power and sample size analyses, etc. One of his favorite responsibilities is co-teaching a Measurement Uncertainty course for engineers, scientists, and other staff members at Sandia. He is an active member of the Statistics in Defense and National Security Section of the ASA.  Dan is a Virginia native and Carolina Panthers fan. His personal interests include: spending time with his kids, mountain biking, and cheesy pick-up lines.


Dr. Stephen V. Crowder, Ph.D.

Dr. Crowder is a Principal Member of Technical Staff in the Statistical Sciences Department at Sandia National Laboratories. He is a subject matter expert in Industrial Statistics, Reliability, and Metrology. Current areas of interest include Design of Experiments, Quality Control, System Reliability, and Measurement Uncertainty. Hobbies include running, baseball, and genealogy research.

Alexander Foss

Alexander Foss

Alex has an M.A. and Ph.D. in biostatistics from the University at Buffalo, studied mathematics and computer science at the University of the South, and completed a dual major in music and in psychological and brain sciences at Indiana University Bloomington. Prior to joining Sandia, Alex has worked as a data analyst at Google, the Children's Hospital of Philadelphia, and Yale University. His statistical interests include the analysis of mixed-type data, cluster analysis, classification, high-dimensional data analysis, statistical computing, and dimension reduction. 

Lauren Hund

Lauren Hund

Lauren Hund joined Sandia National Laboratories as a statistician in 2015. Prior to joining Sandia, she was a faculty biostatistician at the University of New Mexico School of Medicine, engaging in both collaborative and methodological public health research. She has a B.A. in mathematics from Furman University and a Ph.D. in biostatistics from Harvard University, where her research pertained to spatiotemporal modeling and acceptance sampling in global health applications.

Nevin Martin

Nevin Martin

Nevin Martin became a member of the technical staff at Sandia National Laboratories at the start of 2017 after working as a graduate intern for 1.5 years. She holds a M.S. in Statistics from the University of New Mexico and a B.S. in Finance from the University of Arizona. At Sandia, Nevin supports a wide range of projects that include work in uncertainty quantification (UQ), design and analysis of computer experiments, statistical computing, and data visualization.


Matt Martinez

Matthew Martinez joined Sandia National Laboratories in 2011. He has a B.S. in Electrical Engineering with a Supplemental Major in Applied Mathematics from New Mexico State University, a M.S. in Electrical Engineering from Colorado State University, and a Ph.D. in Electrical Engineering from New Mexico State University. His Ph.D. work focused on machine learning and deep learning methods for time series classification using transfer learning. He supports a variety national security projects by providing expertise in signal processing, machine learning, and deep learning. His research interests include the use of constrained machine learning and deep learning methods for scientific applications, time series classification and regression, change point detection, and signal processing.


Leslie M (Lisa) Moore

Lisa Moore joined the Statistical Sciences Department, Org 9136 at Sandia National Laboratories in 2019. She earned a PhD in Mathematics, concentration Statistics, from The University of Texas, Austin. Prior to Sandia, Lisa was employed in the Statistical Sciences Group at Los Alamos National Laboratory. Her research interests include design and analysis of physical experiments or computer simulation-based experiments, factorial design, maximin/minimax distance design, orthogonal or projection array-based Latin Hypercube Sampling, and sensitivity analysis. Project efforts engage Lisa in statistical consulting and collaboration on various problems such as experiment planning for production process development in additive manufacturing, characterizing performance of Li battery packs under various stress conditions, and design and analysis of computer experiments for waste repository performance assessment.


Danny Ries

Daniel Ries joined Sandia National Laboratories in January 2018. He holds a BS in economics from the University of Minnesota and a MS and PhD in statistics from Iowa State University where his dissertation focused on measurement error models for physical activity data from wearable devices. Daniel's work at Sandia is mostly multidisciplinary involving collaboration across with mechanical engineers, imaging scientists, cognitive scientists, among others. His statistical interests include Bayesian neural networks and uncertainty quantification in machine learning, functional data analysis, regression models, and design of experiments.

Lyndsay Shand

Lyndsay Shand

Lyndsay Shand joined Sandia National Laboratories as a statistician in June of 2017. She earned her Ph.D. in Statistics from the University of Illinois-Urbana Champaign and holds a B.S. in Math and B.A. in Spanish from Bucknell University. Prior to Sandia, Lyndsay was a statistical consultant to non-profits and an R&D statistician at Dow Chemical and Dow AgroSciences. Her Ph.D. work involved space-time methods for environmental data and Bayesian hierarchical space-time models applied to disease data. Lyndsay’s research interests include spatial, spatio-temporal statistics and Bayesian hierarchical models applied to the health, environmental and material sciences.

Derek Tucker

Derek Tucker

Derek Tucker joined Sandia National Laboratories in 2014. He has a B.S. and M.S. in Electrical Engineering form Colorado State University, and a Ph.D. in Statistics from the Florida State University. His Ph.D. work involved the development of elastic functional data analysis methods. Before joining Sandia he worked at Naval Surface Warfare Center Panama City Division (NSWC-PCD), Panama City, FL, USA developing algorithms for processing and analyzing synthetic aperture sonar data. At Sandia, he supports a variety of national defense programs by providing statistical expertise in model construction, signal processing, and image registration. His research is focused on pattern theoretic approaches to problems in image analysis, computer vision, signal processing, and functional data analysis.


Lauren Wilson

Lauren Wilson joined Sandia National Laboratories at the end of 2014. She holds a B.S. in Mathematics from Eastern New Mexico University and an M.S. in Statistics from University of New Mexico. Prior to Sandia, Lauren worked for the U.S. Census Bureau as a Mathematical Statistician, producing estimates and constructing sampling frames for various economic surveys. This was followed by time at the Air Force Nuclear Weapons Center where she worked as an Operations Research Analyst, supporting projects primarily related to nuclear effects and safe escape. At Sandia, Lauren supports various engineering projects through data visualization, analysis, modeling, sampling plan development, and other statistical methodologies as necessary.

Adah Zhang

Adah Zhang

Adah Zhang joined Sandia National Laboratories as a statistician in 2016. She received her M.S. in Biostatistics and B.S in Applied Mathematics from Case Western Reserve University in Cleveland, OH. Her M.S. work involved working with large cancer databases, specifically with brain tumors. At Sandia, Adah supports a variety of engineering projects through data visualization, analysis, modeling, and simulation. Additional statistical methodologies used include functional data analysis, uncertainty quantification (UQ), quantification of margins and uncertainties (QMU), and design and analysis of computer experiments.