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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.


Management Team

 



Staff Members

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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.


Stephen Crowder

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.


Christina Deffenbaugh

Christina Deffenbaugh

Christina Deffenbaugh joined the Statistics Team at Sandia Laboratories as an Intern in September of 2014. Her duties include project management, developmental research, analysis of experimental data, and acting as a statistical consultant to a variety of customers. She is currently earning her M.S. in Statistics at the University of New Mexico (UNM). Prior to joining the team, Christina received her B.S. in Forensic Science from Oklahoma Christian University in 2013. She has held other Internships in conjunction with working on her education, including one at the Oklahoma State Bureau of Investigation’s Forensic Science Center where her research focused primarily on the use of Scanning Electron Microscopy Energy Dispersive X-Ray Spectroscopy (SEM-EDS) to analyze gunshot residue (GSR) on clothing. Her intention upon graduating from UNM is to consolidate her interests in both Science and Statistics to pursue a career in Population Genetics.


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.


John Lewis

John Lewis

John Lewis joined Sandia in 2014 shortly after finishing a Ph.D. in Statistics at The Ohio State University. For his dissertation he developed methods for conditioning on insufficient statistics in Bayesian models for the purposes of robustness. At Sandia, John supports a variety of projects using a wide range of statistical methodologies. These methodologies include functional data analysis, design and analysis of computer experiments, uncertainty quantification (UQ), and design of experiments. Other areas of interest include robust estimation techniques, spatiotemporal modeling, and machine learning.


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. 


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 started a pro bono consulting organization to help local and national nonprofits and was a R&D statistical intern 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. At Sandia, Lyndsay supports a variety of research projects through the design and analysis of experiments (DOEx), data visualization and modeling, and many other statistical methodologies. Her research interests include spatial, temporal and space-time statistics.


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.


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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.