Jonathan Berry

Jonathan Berry joined Sandia in 1994 as a Principle Member of Technical Staff after 8 years in academia as a professor. He received his PhD in Computer Science from Rensselaer Polytechnic Institute in 1995.

Jonathan has over 25 refereed publications, predominantly in graph algorithms and computation. He has led a variety of graph-related projects at Sandia, is the leader of Sandia's MultiThreaded Graph Library (MTGL) software project, and is a member of the Graph 500 Supercomputing Benchmark steering committee. He is a recognized leader in the field and gave a plenary presentation on graph analysis for high-performance computing at SIAM Parallel Processing 2010.

• Data Science
• Algorithm design and analysis
• Graph Algorithms

Irina Tezaur

Irina Tezaur is currently a Principal Member of Technical Staff in the Extreme Scales Data Science & Analytics Department in SNL/CA. Since joining Sandia in 2007, Irina has also been a member of the Computational Mathematics and Aerosciences Departments in SNL/NM. She received her Ph.D. from Stanford University’s Institute for Computational & Mathematical Engineering in 2011, and her Bachelors and Masters degrees from the University of Pennsylvania in 2006.

Irina’s research interests include hybridizable discontinuous Galerkin methods, model order reduction, concurrent multiscale coupling methods, numerical solution to partial differential equations, algorithmic design and analysis, and high-performance computing. She has had the opportunity to work in a number of different application areas, including computational fluid dynamics, fluid-structure interactions, computational solid mechanics and climate (more specifically, land-ice and atmospheric) modeling.

• Climate (land-ice and atmospheric) modeling
• Concurrent multi-scale coupling
• Model order reduction

Travis Bauer

Travis Bauer joined Sandia in 2003 after receiving a PhD in Computer Science and Cognitive Science from Indiana University.

Travis has worked extensively in the areas of cognition and data mining. He has focused specifically on the algorithms surrounding statistical text analysis and the engineering of systems that perform this analysis. Travis is one of the core developers of Citrus, a text analysis tool.

• Statistical text analysis
• Data mining
• Cognition

Randy Brost

Randy Brost returned to Sandia in 2011. Previous positions include Eastman Kodak Company and a solar thermal company, SkyFuel. He received his PhD in Computer Science from Carnegie-Mellon University.

Randy’s recent work focused on analysis of geospatial remote sensing data. These data sets can be large and overwhelming, and difficult to comprehend in detail. This challenge is compounded when the desired goal is to analyze changes that occur over time, which requires review and mental comparison of multiple large data sets.

• Geospatial-temporal semantic graphs

Jonathan Crussel

Jonathan Crussel joined Sandia in 2010 and has a Ph.D. in Computer Science from the University of California, Davis.

His current research interests include creating realistic models of networks and systems to evaluate security properties using emulytics, and the development of analytics that are robust in the presence of adversarially crafted input. In the past, his research has included mobile systems, mobile applications, and malware analysis.

• Computer security
• Emulytics

Warren Davis

Warren Davis joined Sandia in 2009 as a Principal Member of the Technical Staff after receiving his PhD in Computer Science from Florida State University.

Since joining Sandia Warren’s research has sought to apply state of the art data mining, machine learning, fuzzy logic, and software engineering knowledge to address important and cutting-edge issues in science and technology. Warren’s expertise is in data mining, neural networks, clustering, fuzzy set theory, genetic algorithms, and object-oriented design.

• Machine learning
• Fuzzy logic
• Software engineering

Stephanie DeJong

Stephanie DeJong joined Sandia as a postdoc in 2016 and transitioned to become a member of the technical staff in 2017. She began working at Sandia after completing her Ph. D. in Analytical Chemistry at the University of South Carolina.

Stephanie’s work has focused on developing algorithms to apply multivariate data analysis techniques to extract meaningful signals from chemical data, with particular focus on developing calibration models for spectroscopic data sets. She has worked on algorithm development for image analysis applications.

• Algorithm Development
• Multivariate Data Analysis
• Spectroscopic Data Analysis

Danny Dunlavy

Danny Dunlavy received his PhD in Applied Mathematics and Computation from the University of Maryland in 2005. He joined Sandia as a John von Neumann Postdoctoral Fellow in 2005 and became a member or the technical staff in 2007. Currently, Danny is a Principal Member of Technical Staff in the Center for Computing Research.

Danny’s career focuses on research in data science and computer and information security. He is primarily focused in machine learning, tensor modeling, and statistical data analysis. His research interests include numerical optimization, numerical linear algebra, machine learning, data mining, tensor decompositions, text analysis, parallel computing, and cyber security.

• Machine learning
• Data mining and data analysis
• Tensor decompositions

Rich Field

Rich Field is a Principal Member of Technical Staff in the Information & Systems Research Center at Sandia National Laboratories in Albuquerque, NM. He received his PhD in structural engineering and applied mathematics from Cornell University in 2004.

Rich has worked in the areas of applied probability, material microstructure, model uncertainty and validation, stochastic differential equations, stochastic mechanics, and random vibrations. Rich is currently focused on data analytics for a variety of applications that include text, graph, and time/spatial dependence.

• Applied probability and statistics
• Data analytics
• Graph theory and social networks

Elizabeth “Scottie-Beth” Fleming

Elizabeth “Scottie-Beth” Fleming is a Senior Member of Technical Staff in the Human Factors department. Prior to joining Sandia in 2016, Scottie-Beth completed her PhD in Aerospace Engineering at the Georgia Institute of Technology, with a minor in Mathematics.

Scottie-Beth has worked in the areas of human decision-making, engineering design, team science, and data analytics. She also has experience with multidisciplinary design optimization and system integration. Her research has utilized both quantitative and qualitative data analysis techniques. Currently, Scottie-Beth uses Human Factors methodologies to examine human decision-making processes and develop recommendations for process improvements which have implications toward increased reliability, safety, and efficiency.

• Human decision-making (measurement and analysis)
• Team sciences
• System/process modeling and integration

Rudy Garcia

Currently a Distinguished Member of the Technical Staff in the Space Missions Architectures and Concepts department, Rudy joined Sandia in 1998 with a BS and MS in Computer Engineering from University of New Mexico and Boston University respectively.

For over 25 years, Rudy has been a software researcher and data architect of software data systems. His focus has been on research and development of data management disciplines such as Data Architecture, Data Security Management, and Data Quality Management. During his tenure at Sandia, Rudy has been researching a number of software systems that collect data, transform the data to standardized formats, store the data securely, and make the data available for efficient retrieval.

• Big Geospatial Data Architectures
• Search Algorithms and Systems
• Model-Based System Engineering

Corey Hudson

Corey Hudson received his PhD in Informatics from the University of Missouri in 2013. He joined Sandia as a Postdoctoral Fellow in 2013 and in 2015 became a Senior Member of the Technical Staff.

Corey’s current work is in the interface of data science, cyber-security and bioinformatics. Corey has focused on bioinformatic tools for the detection of engineered pathogens, the sequencing of highly antibiotic resistant pathogens and tools for prospecting novel biofuel related enzymes from diverse soil samples.

• Macine Learning
• Cyber-security
• Bioinformatics

Joey Ingram

Joey Ingram joined Sandia in 2010 after receiving his Master’s degree in Computer Science from Southern Illinois University.

Joey has research projects focus on applying machine learning to challenging problems in cybersecurity.

• Machine learning
• Cybersecurity

Philip Kegelmeyer

Philip Kegelmeyer, Ph.D. is a Senior Scientist at Sandia National Laboratories in Livermore, CA.

Dr. Kegelmeyer has twenty years’ experience inventing, tinkering with, quantitatively improving, and now, subverting supervised machine learning algorithms (particularly ensemble methods), including investigations into how to accurately and statistically significantly compare such algorithms.

Currently Philip is interested in machine learning and graph algorithms, especially as applied to ugly, obdurate, real-world data which is, perhaps deliberately, actively resistant to analysis. He leads a research effort in "Counter Adversarial Data Analytics". The core idea is to take a vulnerability assessment approach to quantitatively assessing, and perhaps counting, the result of an adversary knowing and adapting to exactly whatever specific data analysis method might be in use.

• Machine Learning
• Graph Algorithms

Tammy Kolda

Tamara (Tammy) Kolda earned her Ph.D. in Applied Mathematics from the University of Maryland in 1997. Before joining Sandia in 1999, Tammy was awarded the Householder Postdoctoral Fellowship in Scientific Computing at Oak Ridge National Lab. In 2010 Tammy became a Distinguished Member of Technical Staff at Sandia.

Tammy has led numerous projects in computational science and data analysis on topics in multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software.

• Tensors Decompositions and eigenvalues
• Graph algorithms
• Machine Learning

Shawn Martin

Shawn Martin received his Ph.D. in Mathematics from Colorado State University in 2001 before joining Sandia in 2002.

Shawn’s work has revolved around the use of computational techniques to analyze and understand large, high-dimensional scientific datasets focusing primarily in bioinformatics, cheminformatics, and computational biology. However, Shawn has been involved in a wide variety of projects ranging from detecting anomalous patterns in computer networks to understanding the genetic basis of leukemia.

• Computational techniques
• Large, high-dimensional data sets

Laura Matzen

Laura Matzen is a Principal Member of the Technical Staff in the Cognitive Science and Systems department. She joined Sandia in 2008 after receiving a Ph.D. in Cognitive Psychology with a concentration in Cognitive Neuroscience from the University of Illinois at Urbana-Champaign.

Her primary research interests lie in using cognitive neuroscience methods to understand how humans process and remember information while performing complex reasoning tasks. She manages Sandia’s Human Performance Lab, conducting research with both behavioral and physiological measures. The HPL’s capabilities include electroencephalography (EEG), transcranial direct current stimulation (tDCS), eye tracking, and virtual reality equipment.

• Visual cognition
• Learning and memory
• Human/System interactions

Laura McNamara

Laura A. McNamara is a Distinguished Member of Technical Staff in the ISR Advanced Exploitation and Human-Systems Integration department at Sandia National Laboratories. She holds a BS in Foreign Service from Georgetown University and a PhD in Anthropology from the University of New Mexico. She spent six years at Los Alamos National Laboratory before joining Sandia National Laboratories in the summer of 2003.

Dr. McNamara’s primary interest is research methods for engaging users and system operators in the design and development of useful, usable, adoptable analytic systems for national security work environments.

• Operator-centric design and evaluation for remote sensing operational interfaces
• Eye-tracking data collection and analysis methods for information foraging research
• Novel methods and frameworks for human-information interaction research in information-dense work environments

Scott Mitchell

Scott Mitchell joined Sandia in 1992, and shortly after earned his PhD in Applied Math from Cornell University. Scott’s technical work currently focuses on sampling, uncertainty quantification and high dimensional space exploration, mesh generation, computational geometry, computer science, discrete math, and information theory.

Scott is currently active in researching and developing sample based techniques, including their uses for mesh generation, integration, and uncertainty quantification. Poisson-disk samplings are popular in computer graphics, for integration-like problems such as texture synthesis, and in simulation for fracture mechanics, where non-randomness would spoil the outcome.

• Sample based techniques
• Uncertainty quantification
• Graph algorithms

Nicole Murchison

Nicole Murchison is a Senior Member of Technical Staff if the Human Factors Department. Nicole joined Sandia in 2016 after receiving her PhD in Psychological Sciences and MS in Industrial Engineering from Purdue University. Along with her PhD in Psychological Sciences, she obtained a certificate in Statistics.

Nicole has worked in areas of decision making, human reliability analysis, and statistics/data analytics, with a primary focus on the former two categories. As part of her research, she developed a decision support system which leverages data-driven/mathematically-based approaches to support multiple user groups. Other major efforts include human reliability analysis to identify, assess, and quantify human failure events. Part of human reliability analysis is to include recommendations to prevent circumstances which lead to human error and/or identify opportunities for error correction. In both instances, Nicole’s background in statistics contributes to the inclusion of rigorous quantitative and qualitative analyses.

• Decision Support Systems
• Human Reliability Analysis
• Design of Experiments

Cindy Phillips

Currently a Senior Scientist in the Center for Computing Research, Cindy Phillips joined Sandia in 1990 after receiving her PhD in Computer Science from the Massachusetts Institute of Technology.

Cindy has worked in areas of combinatorial optimization, algorithm design and analysis, and parallel computation with elements of operations research. She is currently focused in “big data” areas, including streaming, complex (social) network analysis, trajectory analysis, and the design and analysis for high-performance data structures for databases and streaming applications and worked in co-design of algorithms and architectures for extreme-scale future machines with specific consideration of sorting and k-means clustering.

• Combinatorial optimization
• Algorithm design and analysis
• Complex and social network analysis

Ali Pinar

Ali Pinar received his PhD degree in 2001 in computer science from University of Illinois at Urbana-Champaign and worked at Lawrence Berkeley National Laboratories before joining Sandia in 2008.

Ali’s research is on design and analysis of combinatorial algorithms for practical applications. His work spans the areas of parallel computing, sparse matrix computations, electric power systems, graph mining, social networks, cyber networks, and interconnection networks.

• Modeling and analysis of complex networks
• Graph algorithm
• Graph minings

Jaideep Ray

Currently a Distinguished Member of Technical Staff in the Computational Science and Analysis Center, Jaideep is an aerodynamicist by training. He received his Ph.D. in Aerospace Engineering from Rutgers, The State University of New Jersey, in 1999. He joined Sandia in the same year, and has worked in Computational and Information Sciences since 2000.

Jaideep has worked in developing numerical methods for reactive flows, parallel computing and solving inverse problems, especially for computationally expensive problems that one faces in engineering sciences. Over the last decade, his interests have revolved around estimating rough fields from sparse data e.g., inferring fossil-fuel CO2 emission fields, and developing methods to calibrate climate and engineering models. He is particularly interested in developing scalable sampling methods for statistical learning. His publications and open-source software packages are to be found at his website.

• Bayesian inverse problems, random field models, scalable Markov chain Monte Carlo methods
• High performance computing, asynchronous parallel algorithms
• Computational fluid dynamics, high-order discretizations on adaptively refined meshes

Danny Rintoul

After earning his PhD in Physics from Purdue in 1994, Danny spent three years at Princeton working on statistical physics and operations research problems as a research associate. He joined Sandia in 1997, where he has focused primarily on modeling large, complex problems on high-performance computing architectures.

After 11 years in management, Danny returned to research in 2013 working primarily in the area of using high-performance computing and advanced mathematical modeling techniques to solve complex real-world problems. Danny has worked in various applications including material science, biology, computational geometry, and areas of national defense.

• Efficient computational techniques for analyzing and comparing trajectories

Kathryn Rodhouse

Before joining Sandia in 2012, Kathryn Rodhouse received her M.S. in Computer Engineering from Missouri University of Science & Technology.

Kathryn has experience researching, adapting and applying data modeling, ensembles of decision trees and clustering algorithms to problems focused on issues of national security. Additionally, Kathryn has a worked in testing and comparing robustness and resiliency of different machine learning models for a given problem. She has also invested significant time researching applications of data mining techniques to static and dynamic software analysis.

• Feature engineering to facilitate modeling of textual data
• Network streaming data

J. Derek Tucker

Currently a Principal Member of the Technical Staff in the Statistical Sciences department. Derek joined Sandia in 2014 after receiving his Ph.D. in Statistics from the Florida State University under the advisement of Dr. Anuj Srivastava.

Derek works in the areas of functional data analysis, point processed theory, and statistical learning. He is currently focused on registration problems and applying uncertainty quantification to functional data, specifically in the area of image registration where understanding the registration confidence is key. He is also currently working on handling missing data for spatio-temporal point process models which are self-exciting in behavior. For more information: http://research.tetonedge.net

• Functional Data Analysis
• Statistical Learning
• Spatio-Temporal Point Processes

Craig Ulmer

Craig Ulmer received his Ph.D. in Electrical Engineering from the Georgia Institute of Technology in 2002 before joining Sandia.

Craig is a Computer Engineer that specializes in utilizing cluster computers to solve data intensive problems. While he typically likes to focus on acquiring and analyzing data, Craig often must deal with the dirty logistics of cluster hardware and software.

• Data Anlysis
• HPC Interconnects
• DevOps

Jeremy Wendt

Jeremy Wendt joined Sandia in 2010 after receiving a PhD in Computer Science from the University of North Carolina.

Jeremy’s research has spanned the areas of text analysis, graph analysis, machine learning, and social analytics.

• Text and graph analysis
• Machine learning
• Social Analytics

Patrick Widener

Patrick Widener is a Principal Member of Technical Staff in the Center for Computing Research. He joined Sandia in 2012 and holds a Ph.D. in Computer Science from the Georgia Institute of Technology.

During his career at Sandia, Patrick has worked on projects involving data fusion for enterprise and scientific data, efficient streaming middleware for data movement and storage, and performance effects of fault-tolerance protocols for large computational scientific applications. His current activities concentrate on the design and development of system software to support large-scale data-centric computational science, including interfaces with heterogeneous storage software and hardware, tools for examining performance interference caused by in-situ analytics, and software architectures for describing and exchanging data in computational science workflows.

• Local and remote collaborative data-intensive workflows
• Enterprise-scale information services
• Streaming data middleware and applications