Statistical Sciences Staff
Department Overview: 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.
Adele Doser (Manager): Adele Doser joined Sandia National Laboratories in 2001. She has been the manager of the statistical sciences group since April, 2019. Adele obtained her B.S. from Michigan Tech, and her M.S. and Ph.D. from University of Arizona, all in Electrical Engineering. Adele’s experiences include being a postdoc at Los Alamos National Laboratory, an assistant professor at University of Texas at Dallas, and an adjunct associate professor at University of New Mexico. Her research interests include signal processing, wavelets, and machine learning. Adele enjoys hiking, skiing, cycling, and world travel.
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.
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.
Benjamin Emery: Ben moved from Vermont to join the Statistical Sciences team October 2019. While studying at the University of Vermont, he received a B.S. in Statistics, B.S. in Physics, and M.S. in Complex Systems. His master’s thesis investigated collective human behavior in response to natural disasters, a subject inspired by members of his family living through the devastating effects of Hurricane Maria in Puerto Rico. Ben’s research has focused on subject areas such as climate science, food systems, and computational social science, with a specific interest in the emergent behavior of complex interactions between the components in a system. Outside of business hours, Ben can likely be found stuck to a cactus after falling off his mountain bike.
Casey Jelsema: Casey joined the Statistical Sciences Department at Sandia National Laboratories in January 2020. He was previously a faculty member in the departments of Statistics and Biostatistics at West Virginia University. His statistical research includes reduced-rank methods for large spatial datasets, particularly in the case of non-Normality, and methods for order-restricted inference. He also likes statistical computing, statistical consultation for a variety of disciplines, and – as he was a professor for four years – teaching others about statistical methods. In his spare time, Casey enjoys playing ice hockey, reading, games of various sorts, hiking with his dogs, and has been delving into a new hobby of woodworking.
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.
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.
Derek Tucker: Derek Tucker joined Sandia National Laboratories in 2014. He has a B.S. and M.S. in Electrical Engineering from 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.
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.
Gabriel Huerta: Gabriel received his Ph.D. and M.S in Statistics from Duke University. Previously, he was on the faculty at the Department of Mathematics and Statistics at the University of New Mexico. He joined Sandia in 2018 as a distinguished member of the technical staff. He is the Associate Editor for the journals Bayesian Analysis, Journal of Uncertainty Quantification and Environmetrics. Gabriel Huerta’s research interests include Bayesian methods, time series, space and space/time models, extreme value analysis, and UQ/statistical learning. His major projects at Sandia include Bayesian model calibration in material sciences applications, UQ for combined EM/mechanical codes, connections between UQ and machine learning methods, Bayesian approaches for reliability assessment, and development of surrogates for ML applications to non-linear material modeling.
India Dytzel: India Dytzel joined the Statistical Sciences Department at Sandia National Laboratories in December 2019. She is a doctoral student at New Mexico Tech University. Her research interests include statistics, probability, and machine learning. In her spare time, she enjoys exploring New Mexico and collecting books.
Jason Adams: Jason Adams joined the Statistical Sciences Department at Sandia National Laboratories in February 2020. He received a Bachelor of Science degree in Mathematics from Southern Utah University as well as a Master of Science and PhD in Statistics from the University of Nebraska-Lincoln. His research interests include machine learning, computational statistics, causal inference, and statistical consulting. Jason enjoys being outdoors, fishing, cooking, and spending time with his wife and son.
Katherine Goode: Katherine joined the Statistical Sciences Department at Sandia National Laboratories as an intern in December 2019. She has a bachelor’s degree in mathematics from Lawrence University and a master’s degree in statistics from the University of Wisconsin Madison. Currently, she is a PhD student studying statistics at Iowa State University. Her research is on the explainability of machine learning algorithms, and she will be helping with research on this topic during her internship with Sandia. Katherine enjoys playing sports, traveling, and outdoor activities.
Lauren C. Wilson: Lauren C. Wilson joined Sandia National Laboratories in December 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. Lauren’s prior experience includes time as a Mathematical Statistician with the US Census Bureau and an Operations Research Analyst with the Air Force Nuclear Weapons Center. At Sandia, Lauren supports various nuclear deterrence projects through data analysis & visualization, modeling, sampling plan development, simulation, and experimental design, to name a few, as well as co-instructs several statistics courses offered across the labs. Her hobbies include dog training, cycling, and carpentry.
Lekha Patel: Lekha joined the Statistical Sciences Department at Sandia National Laboratories in January 2020 after completing both her Bachelor and PhD in Mathematics from Imperial College London. Her research interests broadly lie in the field of statistical signal processing for point patterns. She has a specific focus on the modeling of partially observed spatio-temporal point processes and their inference via computational Bayesian methods, with previous application in biology and cyber-security. Outside of work, she enjoys cooking, hiking and learning Spanish.
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.
Mason Parsons: Mason Parsons joined the Statistical Sciences Department at Sandia National Laboratories in March of 2019. He holds a B.S. in Astrophysics and an M.S. in Statistics from the University of New Mexico. Prior to Sandia, Mason spent 14 years as a DoD contractor providing modeling, simulation, and data analysis support to several directed-energy and space programs. His work at Sandia has involved design of experiments, exploratory data analysis, data visualization, and quantification of margins and uncertainties. Other interests include nonparametric regression, tree-based classification, and resampling methods. Outside of work he enjoys going on adventures with his kids and playing the bagpipes.
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.
Reese Davies: Reese Davies has a B.S. in Applied Mathematics and M.S in Applied Statistics and from New Mexico State University. He joined Sandia National Laboratories in the beginning of 2018 as a member of the technical staff. Reese supports a variety of projects at Sandia, focusing on Data Visualization, Design of Experiments, and Exploratory Data Analysis. He is the acting Treasurer of the Albuquerque chapter of the ASA. Current statistical interests include Machine Learning, Margin Assessment, and System Reliability. Reese is in training to teach multiple statistically-oriented courses throughout the Laboratories. Hobbies include finance, golf, and being a contrarian.
Tabytha Perez: Tabytha Perez is one of the student interns for the Statistical Sciences department. She joined the team in August 2019 after graduating in May 2019 with her Bachelor of Science in Statistics. She is currently pursuing her Master of Science also in Statistics at the University of New Mexico. She is still learning and is currently working under the reliability team. In her free time, Tabytha loves to read, go to dance fitness, and give her friends her endless support.