Daniel Dunlavy

Machine Intelligence and Vis

Author profile picture

Machine Intelligence and Vis

dmdunla@sandia.gov

(505) 284-6092

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1327

Biography

Danny Dunlavy is a Principal Member of Technical Staff in the Scalable Analysis and Vis Department of the Center for Computing Research at Sandia National Laboratories in Albuquerque, NM. His research interests include numerical optimization, numerical linear algebra, machine learning, data mining, tensor decompositions, graph algorithms, text analysis, parallel computing, and cyber security.

Expanded Personal Web Page

Education

  • Ph.D., Applied Mathematics and Scientific Computation, University of Maryland, College Park, 2005
  • M.S., Applied Mathematics and Scientific Computation, University of Maryland, College Park, 2003
  • M.S., Applied Mathematics, Western Michigan University, 2001
  • B.A., Computer Studies, Northwestern University, 1994

Publications

Oscar Lopez, Daniel Dunlavy, Richard B. Lehoucq, (2022). Zero-Truncated Poisson Regression for Multiway Count Data Conference on Random Matrix Theory and Numerical Linear Algebra Document ID: 1540237

Daniel Dunlavy, Richard B. Lehoucq, Oscar Lopez, (2022). Low-Rank Tensor Decompositions for Large Sparse Count Data 8th European Seminar on Computing (ESCO 2022) – ONLINE Document ID: 1539971

Jeremy Myers, Daniel Dunlavy, (2022). A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization https://www.osti.gov/search/identifier:1865529 Document ID: 1492758

Jeremy Myers, Daniel Dunlavy, (2022). Cyclic GCP-CPAPR Hybrid SIAM Conference on Parallel Processing for Scientific Computing (PP22) Document ID: 1438032

Oscar Lopez, Richard B. Lehoucq, Daniel Dunlavy, (2022). Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data https://www.osti.gov/search/identifier:1841834 Document ID: 1416222

Daniel Dunlavy, Peter Alexander Chew, (2021). Document Retrieval and Ranking using Similarity Graph Mean Hitting Times https://www.osti.gov/search/identifier:1835671 Document ID: 1403581

Sean Isaac Geronimo Anderson, Keita Teranishi, Daniel Dunlavy, Jee Choi, (2021). Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures 2021 IEEE High Performance Extreme Computing Virtual Conference Document ID: 1356071

Jeremy Myers, Daniel Dunlavy, (2021). Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency 2021 IEEE High Performance Extreme Computing Virtual Conference Document ID: 1356072

Stephen Lecler Olivier, Nathan David Ellingwood, Jonathan W. Berry, Daniel Dunlavy, (2021). Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications 2021 IEEE High Performance Extreme Computing Virtual Conference Document ID: 1356073

Jeremy Myers, Daniel Dunlavy, (2021). Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency 2021 IEEE High Performance Extreme Computing Virtual Conference Document ID: 1344776

Stephen Lecler Olivier, Nathan David Ellingwood, Jonathan W. Berry, Daniel Dunlavy, (2021). Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications 2021 IEEE High Performance Extreme Computing Virtual Conference Document ID: 1344909

Sean Isaac Geronimo Anderson, Keita Teranishi, Daniel Dunlavy, Jee Choi, (2021). Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures 2021 IEEE High Performance Extreme Computing Virtual Conference Document ID: 1344911

Daniel Dunlavy, Daniel Dunlavy, (2021). Questa High Performance Data Analytics 2020 Sandia CIS ERB Reivew https://www.osti.gov/search/identifier:1855745 Document ID: 1279481

Jeremy Myers, Daniel Dunlavy, Keita Teranishi, David S Hollman, (2020). Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software: Extended Analysis https://www.osti.gov/search/identifier:1706215 Document ID: 1219819

Scott Heidbrink, Kathryn Nicole Rodhouse, Daniel Dunlavy, Alexis Cooper, Xin Zhou, (2020). Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities https://www.osti.gov/search/identifier:1670527 Document ID: 1197164

Alexis Cooper, Xin Zhou, Scott Heidbrink, Daniel Dunlavy, (2020). Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models https://www.osti.gov/search/identifier:1668457 Document ID: 1197427

Keita Teranishi, Daniel Dunlavy, Jeremy Myers, Richard Frederick Barrett, (2020). SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data 2020 IEEE High Performance Extreme Computing Virtual Conference https://www.osti.gov/search/identifier:1822300 Document ID: 1196276

Alexis Cooper, Xin Zhou, Daniel Dunlavy, Scott Heidbrink, (2020). Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models https://www.osti.gov/search/identifier:1668929 Document ID: 1196657

Scott Heidbrink, Kathryn Nicole Rodhouse, Daniel Dunlavy, (2020). Multimodal Deep Learning for Flaw Detection in Software Programs https://www.osti.gov/search/identifier:1660805 Document ID: 1196136

Jeremy Myers, Daniel Dunlavy, Keita Teranishi, David S Hollman, (2020). Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software 2020 IEEE High Performance Extreme Computing Conference https://www.osti.gov/search/identifier:1820264 Document ID: 1196130

Scott Heidbrink, Daniel Dunlavy, Kathryn Nicole Rodhouse, (2020). Multimodal Deep Learning for Flaw Detection in Software Programs Sandia MLDL Workshop https://www.osti.gov/search/identifier:1811608 Document ID: 1172883

Daniel Dunlavy, (2020). Tensor Decompositions for Analyzing Multi-Way Data CSRI Summer Program – Seminar Series https://www.osti.gov/search/identifier:1809204 Document ID: 1162308

Jonathan Bisila, Daniel Dunlavy, Zoe Nellie Gastelum, Craig D. Ulmer, (2020). Topic Modeling With Natural Language Processing For Identification Of Nuclear Proliferation-relevant Scientific And Technical Publications INMM Annual Meeting https://www.osti.gov/search/identifier:1805335 Document ID: 1150938

Keita Teranishi, Daniel Dunlavy, Jeremy Myers, Richard Frederick Barrett, (2020). SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data 2020 IEEE High Performance Extreme Computing Conference (HPEC ?20) https://www.osti.gov/search/identifier:1798425 Document ID: 1139445

Jeremy Myers, Daniel Dunlavy, Keita Teranishi, David S Hollman, (2020). Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software 2020 IEEE High Performance Extreme Computing Conference (HPEC’20) https://www.osti.gov/search/identifier:1798424 Document ID: 1139446

Jonathan Bisila, Daniel Dunlavy, Zoe Nellie Gastelum, Craig D. Ulmer, (2020). Topic Modeling With Natural Language Processing For Identification Of Nuclear Proliferation-relevant Scientific And Technical Publications INMM Annual Meeting https://www.osti.gov/search/identifier:1807413 Document ID: 1139272

Jonathan Bisila, Daniel Dunlavy, Zoe Nellie Gastelum, (2020). Natural Language Processing for Topic Identification Supporting Document Search and Identification for Nuclear Proliferation Detection Confernce on Data Analysis (CoDA) https://www.osti.gov/search/identifier:1766759 Document ID: 1091643

Scott Heidbrink, Kathryn Nicole Rodhouse, Daniel Dunlavy, (2020). Detecting Flaws in Software Programs using Multimodal Deep Learning Conference on Data Analysis https://www.osti.gov/search/identifier:1766760 Document ID: 1092082

Keita Teranishi, David S Hollman, Jeremy Myers, Daniel Dunlavy, (2020). Performance and Parallelization of CP-Alternate Poisson Regression Sparse Tensor Decomposition SIAM Conference on Parallel Processing for Scientific Computing (PP20) https://www.osti.gov/search/identifier:1766745 Document ID: 1092166

Daniel Dunlavy, Jonathan Bisila, Zoe Nellie Gastelum, (2020). Topic Modeling with Natural Language Processing for Identification of Safeguards-Relevant Scientific and Technical Publications INMM Annual Meeting Document ID: 1091225

Daniel Dunlavy, Kathryn Nicole Rodhouse, Scott Heidbrink, (2020). Detecting Flaws in Software Programs using Multimodal Deep Learning Conference on Data Analysis (CoDA) Document ID: 1079640

Daniel Dunlavy, Jonathan Bisila, Zoe Nellie Gastelum, (2020). Natural Language Processing for Topic Identification supporting Document Search and Identification for Nuclear Proliferation Detection Conference on Data Analysis Document ID: 1079155

Daniel Dunlavy, Fulton Wang, Michael Wolf, Nathan David Ellingwood, (2019). LDRD Final Report: Modeling Complex Relationships in Large-Scale Data using Hypergraphs https://www.osti.gov/search/identifier:1595914 Document ID: 1055877

Timothy Malcolm Shead, Hemanth Kolla, Aditya Konduri, Gabriel Papoola, Warren Leon Davis, Daniel Dunlavy, Kevin Reed, (2019). A Framework for In-Situ Anomaly Detection in HPC Environments Supercomputing 2019 https://www.osti.gov/search/identifier:1642180 Document ID: 1031547

Keita Teranishi, David S Hollman, Jeremy Myers, Richard Frederick Barrett, Daniel Dunlavy, (2019). Load balancing strategy of Parallel Performance Portable Sparse CP-APR Decomposition Document ID: 1020380

Keita Teranishi, David S Hollman, Richard Frederick Barrett, Jeremy Myers, Daniel Dunlavy, (2019). Performance and Parallelization of CP-Alternate Poisson Regression Sparse Tensor Decomposition Conference on AI and Tensor Factorizations for Physical, Chemical and Biological Systems https://www.osti.gov/search/identifier:1641999 Document ID: 1009885

Keita Teranishi, David S Hollman, Jeremy Myers, Richard Frederick Barrett, Daniel Dunlavy, (2019). Performance and Parallelization of CP-Alternate Poisson Regression Sparse Tensor Decomposition AI and Tensor Factorizations for Physical, Chemical and Biological Systems Document ID: 1009819

Daniel Dunlavy, (2019). Third Annual Machine Learning and Deep Learning (MLDL) Workshop Machine Learning and Deep Learning (MLDL) Workshop https://www.osti.gov/search/identifier:1645722 Document ID: 997874

Timothy Malcolm Shead, Daniel Dunlavy, Hemanth Kolla, Aditya Konduri, Gabriel ANUOLUWAPO Popoola, Warren Leon Davis, William Philip Kegelmeyer, Kevin Reed, Julia Ling, (2019). A Framework for In-Situ Anomaly Detection in HPC Environments Ldav 2019 https://www.osti.gov/search/identifier:1640853 Document ID: 974135

Keita Teranishi, David S Hollman, Daniel Dunlavy, Richard Frederick Barrett, (2019). Development of parallel sparse CP-APR tensor decomposition solvers Kokkos User Group Meeting https://www.osti.gov/search/identifier:1639958 Document ID: 959846

Keita Teranishi, Daniel Dunlavy, Richard Frederick Barrett, Tamara G. Kolda, Christopher Forster, (2019). Performance portable parallel sparse CP-APR tensor decompositions SIAM Conference on Computational Science and Engineering (CSE19) https://www.osti.gov/search/identifier:1639247 Document ID: 935512

Karen D. Devine, Erik Gunnar Boman, Daniel Dunlavy, Tamara G. Kolda, Michael Wolf, (2018). Exploiting Scientific Software to Solve Problems in Data Analytics IPAM Workshop IIIHPC for Computationally and Data-Intensive Problems https://www.osti.gov/search/identifier:1574573 Document ID: 889174

Keita Teranishi, Richard Frederick Barrett, Christopher (NVIDIA) Forster, Daniel Dunlavy, Tamara G. Kolda, (2018). Performance portable parallel CP-APR tensor decompositions Sparse Days 2018 https://www.osti.gov/search/identifier:1806849 Document ID: 875766

Daniel Dunlavy, Timothy Malcolm Shead, Daniel Dunlavy, Aditya Konduri, Hemanth Kolla, Daniel Dunlavy, William Philip Kegelmeyer, Warren Leon Davis, (2018). Embedding Python for In-Situ Analysis https://www.osti.gov/search/identifier:1734473 Document ID: 842411

Gary Joseph Saavedra, Daniel Dunlavy, (2018). Static Source Code Analysis Using Neural Networks Machine Learning and Deep Learning Workshop https://www.osti.gov/search/identifier:1575163 Document ID: 842144

Daniel Dunlavy, (2018). Building a Machine Learning and Deep Learning (MLDL) Community at Sandia 2018 MLDL Workshop https://www.osti.gov/search/identifier:1574864 Document ID: 841916

Wesley Alexander Brooks, Daniel Dunlavy, Jennifer Galasso, Christopher M. Howerter, Nicholas Jacobs, Christine Flora Lai, (2018). Applying Natural Language Processing (NLP) to RF Signal Patterns Machine Learning and Deep Learning Workshop 2018 https://www.osti.gov/search/identifier:1573955 Document ID: 841695

Richard Frederick Barrett, Christopher (NVIDIA) Forster, Daniel Dunlavy, Tamara G. Kolda, Keita Teranishi, (2018). Performance portable parallel CP-APR tensor decompositions Sparse Days 18 Document ID: 830463

Richard Frederick Barrett, Christopher (NVIDIA) Forster, Tamara G. Kolda, Daniel Dunlavy, (2018). A performance portable parallel CP-APR decomposition Sparse Days 18 Document ID: 830441

Daniel Dunlavy, (2018). Scalable Tensor Factorizations on Multiple Architectures Tricap 2018 https://www.osti.gov/search/identifier:1526817 Document ID: 808764

Daniel Dunlavy, Richard Frederick Barrett, Richard B. Lehoucq, Tamara G. Kolda, Keita Teranishi, (2018). A Performance Portable Sparse Tensor Decomposition for Poisson Regression Problems Cis Erb Document ID: 807952

Warren Leon Davis, Daniel Dunlavy, William Philip Kegelmeyer, Hemanth Kolla, Aditya Konduri, Timothy Malcolm Shead, Kevin Reed, (2018). In-Situ Machine Learning for Intelligent Data Capture in HPC Simulations CIS External Advisory Board Meeting https://www.osti.gov/search/identifier:1524824 Document ID: 807593

Daniel Dunlavy, (2018). SparTen: Software for Sparse Tensor Decompositions Document ID: 796948

Daniel Dunlavy, Peter A. Chew, (2018). Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging https://www.osti.gov/search/identifier:1254278 Document ID: 443150

Aditya Konduri, Hemanth Kolla, Julia Ling, William Philip Kegelmeyer, Daniel Dunlavy, Timothy Malcolm Shead, Warren Leon Davis, (2018). Event Detection In Multi-variate Scientific Simulations Using Feature Anomaly Metrics SIAM Conference on Parallel Processing for Scientific Computing (PP18) https://www.osti.gov/search/identifier:1499084 Document ID: 771997

Michael Wolf, Daniel Dunlavy, Richard B. Lehoucq, Jonathan W. Berry, Daniel (Rice) Bourgeois, (2018). TriData: High Performance Linear Algebra-Based Data Analytics Siam Pp18 https://www.osti.gov/search/identifier:1497538 Document ID: 761291

Christopher James Forster, Keita Teranishi, Daniel Dunlavy, Tamara G. Kolda, (2017). Analysis of Performance and Portability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures using Kokkos Nvidia Gtc Document ID: 727583

Christopher James Forster, Keita Teranishi, Daniel Dunlavy, Tamara G. Kolda, Christopher James Forster, Christopher James Forster, (2017). Analysis of Performance and Portability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures Siam Pp 2018 Document ID: 703120

Michael Wolf, Daniel Dunlavy, Richard B. Lehoucq, Daniel Bourgeois (Rice), Alicia Marie Klinvex, (2017). TriData: High Performance Linear Algebra-Based Data Analytics Siam Pp18 Document ID: 670442

Timothy Malcolm Shead, Aditya Konduri, Hemanth Kolla, Daniel Dunlavy, William Philip Kegelmeyer, (2017). Embedding Python for In-Situ Analysis In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization https://www.osti.gov/search/identifier:1467787 Document ID: 670644

Eric T. Phipps, Tamara G. Kolda, Clifford Isaac Anderson-Bergman, Karen D. Devine, Daniel Dunlavy, David Hong, Richard Vuduc, Jaijai Li, Jeff Young, Grey Ballard, (2017). Parallel Tensor Decompositions for Massive, Heterogeneous, Incomplete Data CIS External Review https://www.osti.gov/search/identifier:1465080 Document ID: 659780

Keita Teranishi, Christopher James Forster, Daniel Dunlavy, Tamara G. Kolda, (2017). Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures Advanced Supercomputing Environment Seminat University of Tokuyo https://www.osti.gov/search/identifier:1462627 Document ID: 658763

Dalton Russell Cole, Scott Heidbrink, Daniel Dunlavy, (2017). FrogEye: Joint analysis of source code and binaries using Machine Learning Intern Symposium 2017 https://www.osti.gov/search/identifier:1507276 Document ID: 637654

Keita Teranishi, Christopher James Forster, Daniel Dunlavy, Tamara G. Kolda, (2017). Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures The Laboratory for Physical Science, HPC Analytics Workshop https://www.osti.gov/search/identifier:1458407 Document ID: 626650

Richard B. Lehoucq, Erik Gunnar Boman, Karen D. Devine, Jonathan W. Berry, Daniel Dunlavy, Michael Wolf, Van (LLNL) Henson, Geoff (LLNL) Sanders, (2017). A computational spectral graph theory tutorial Householder Symposium XX https://www.osti.gov/search/identifier:1456850 Document ID: 626244

Christopher James Forster, Keita Teranishi, Greg Edward Mackey, Daniel Dunlavy, Tamara G. Kolda, (2017). Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures 2017 SIAM Annual Meeting Document ID: 579023

Alicia Marie Klinvex, Michael Wolf, Daniel Dunlavy, (2016). TriData: Applying Trilinos to Data Analysis Trilinos User-Developer Group Meeting 2016 https://www.osti.gov/search/identifier:1408343 Document ID: 553039

Warren Leon Davis, Daniel Dunlavy, Yevgeniy Vorobeychik, Karin Butler, Chris Forsythe, Matthew Letter, Nicole Murchison, Kevin S. Nauer, (2016). Using Machine Learning in Adversarial Environments https://www.osti.gov/search/identifier:1563076 Document ID: 530819

Michael Wolf, Michael Wolf, Alicia Marie Klinvex, Alicia Marie Klinvex, Daniel Dunlavy, Daniel Dunlavy, (2016). Advantages to Modeling Relational Data using Hypergraphs versus Graphs 2016 IEEE High Performance Extreme Computing Conference https://www.osti.gov/search/identifier:1376791 Document ID: 507274

Michael Wolf, Alicia Marie Klinvex, Daniel Dunlavy, (2016). Advantages to Modeling Relational Data using Hypergraphs versus Graphs 2016 IEEE High Performance Extreme Computing Conference https://www.osti.gov/search/identifier:1529798 Document ID: 476141

Michael Wolf, Daniel Dunlavy, Alicia Marie Klinvex, (2016). Hypergraph Exploitation for Data Sciences Graph Exploitation Symposium https://www.osti.gov/search/identifier:1367108 Document ID: 443111

Alicia Marie Klinvex, Michael Wolf, Daniel Dunlavy, (2016). Clustering network data through effective use of eigensolvers and hypergraph models Fourteenth Copper Mountain Conference On Iterative Methods https://www.osti.gov/search/identifier:1347490 Document ID: 419877

Alicia Marie Klinvex, Michael Wolf, Daniel Dunlavy, (2016). Clustering network data through effective use of eigensolvers and hypergraph models Fourteenth Copper Mountain Conference On Iterative Methods Document ID: 397377

Daniel Dunlavy, Michael Wolf, (2015). Clustering network data using graphs, hypergraphs, and tensors Statistical and computational challenges in networks and cybersecurity https://www.osti.gov/search/identifier:1251364 Document ID: 264950

Warren Leon Davis, Daniel Dunlavy, James C. Forsythe, Yevgeniy Vorobeychik, (2015). Machine Learning in Adversarial Environments Cis Erb Document ID: 243480

Daniel Dunlavy, (2015). Clustering Network Data using Graphs, Hypergraphs, and Tensors Statistical and computational challenges in networks and cybersecurity Document ID: 243048

Warren Leon Davis, Daniel Dunlavy, (2014). Hybrid Methods for Cybersecurity Analysis LDRD Final Report https://www.osti.gov/search/identifier:1147641 Document ID: 5331477

Timothy Malcolm Shead, Daniel Dunlavy, (2013). Network and Ensemble Enabled Entity Extraction in Informal Text (NEEEEIT) Final Report https://www.osti.gov/search/identifier:1115263 Document ID: 5329496

Warren Leon Davis, Daniel Dunlavy, Christopher Nebergall, (2013). The Hybrid Toolkit Sandia Technology Showcase https://www.osti.gov/search/identifier:1732189 Document ID: 5327101

Dwayne L. Knirk, Daniel Dunlavy, Cynthia Ann Phillips, David G. Robinson, Jiqiang Guo, Daniel Nordman, Alyson Wilson, (2013). Community Detection: A Bayesian Approach and the Challenge of Evaluation Structure, Statistical Inference, and Dynamics in NetworksFrom Graphs to Rich Data https://www.osti.gov/search/identifier:1079010 Document ID: 5322240

Dwayne L. Knirk, Daniel Dunlavy, Cynthia Ann Phillips, David G. Robinson, Jiqiang Guo, Daniel Nordman, Alyson Wilson, (2012). Community Detection: A Bayesian Approach and the Challenge of Evaluation Graph Exploitation Workshop https://www.osti.gov/search/identifier:1067771 Document ID: 5306910

Dwayne L. Knirk, Daniel Dunlavy, Cynthia Ann Phillips, David G. Robinson, Jiqiang Guo, Daniel Nordman, Alyson Wilson, (2012). Parallel Bayesian Methods for Community Detection SIAM Conference on Parallel Computing https://www.osti.gov/search/identifier:1069032 Document ID: 5304498

Andrew T. Wilson, Daniel Dunlavy, Timothy Shead, (2011). TopicView: Visually Comparing Topic Models of Text Collections 23rd IEEE International Conference on Tools with Artificial Intelligence https://www.osti.gov/search/identifier:1661491 Document ID: 5301030

Andrew T. Wilson, Daniel Dunlavy, Timothy M. Shead, (2011). TopicView Poster Preview IEEE VisWeek 2011 Workshop on Interactive Visual Text Analytics for Decision Making https://www.osti.gov/search/identifier:1661532 Document ID: 5300890

Andrew T. Wilson, Daniel Dunlavy, Timothy M. Shead, (2011). TopicView: Understanding Document Relationships Using Latent Dirichlet Allocation Models Interactive Visual Text Analytics for Decision Making Workshop https://www.osti.gov/search/identifier:1106132 Document ID: 5298215

Ali Pinar, Daniel Dunlavy, (2011). Compressively sensed complex networks SIAM Annual Meeting https://www.osti.gov/search/identifier:1021676 Document ID: 5284701

Andrew T. Wilson, Daniel Dunlavy, Timothy M. Shead, (2011). TopicView: Visually Comparing Semantic Models IEEE Conference on Visual Analytics Science and Technology (IEEE VAST) https://www.osti.gov/search/identifier:1108535 Document ID: 5293372

Tamara G. Kolda, Daniel Dunlavy, (2010). Shifted Power Method for Computing Tensor Eigenpairs https://www.osti.gov/search/identifier:1005408 Document ID: 5286689

Tamara G. Kolda, Daniel Dunlavy, Evrim Acar, Morten Morup, (2010). Scalable Tensor Factorizations with Incomplete Data SIAM 2010 Annual Meeting https://www.osti.gov/search/identifier:1021587 Document ID: 5285035

Tamara G. Kolda, Daniel Dunlavy, Evrim Acar, (2010). Link Prediction on Evolving Graphs using Matrix and Tensor Factorizations BIT 50 Trends in Numerical Computing https://www.osti.gov/search/identifier:1021699 Document ID: 5283623

Evrim NMN Acar Ataman, Daniel Dunlavy, (2010). An Optimization Approach for Fitting Canonical Tensor Decompositions https://www.osti.gov/search/identifier:978916 Document ID: 5269532

Tamara G. Kolda, Evrim NMN Acar Ataman, Daniel Dunlavy, (2009). Link Prediction on Evolving Data using Matrix and Tensor Factorizations IEEE International Conference on Data Mining series (ICDM 2009) https://www.osti.gov/search/identifier:1141980 Document ID: 5273317

Evrim NMN Acar Ataman, Tamara G. Kolda, Daniel Dunlavy, (2009). An Optimization Approach for Fitting Canonical Tensor Decompositions SIAM Annual Meeting https://www.osti.gov/search/identifier:1142188 Document ID: 5273427

Evrim NMN Acar Ataman, Tamara G. Kolda, Daniel Dunlavy, (2009). The Canonical Tensor Decomposition and Its Applications to Data Analysis Linear Algebra and Optimization Seminar https://www.osti.gov/search/identifier:1141818 Document ID: 5272425

Evrim NMN Acar Ataman, Tamara G. Kolda, Daniel Dunlavy, (2009). Link Prediction on Evolving Data using Tensor Factorizations SIAM Conference on Computational Science and Engineering https://www.osti.gov/search/identifier:1142541 Document ID: 5270013

Tamara G. Kolda, Evrim NMN Acar Ataman, Daniel Dunlavy, (2008). CPOPT: Optimization for fitting CANDECOMP/PARAFAC models Computational Algebraic Statistics, Theories and Applications (CASTA2008) https://www.osti.gov/search/identifier:970228 Document ID: 5266474

Patricia J. Crossno, Daniel Dunlavy, Timothy Shead, (2008). Using Visualization for Relevancy Feedback Tuning of Text Analysis Algorithms GFX Cafe Seminar UNM https://www.osti.gov/search/identifier:1712862 Document ID: 5261079

Stuart L. Kupferman, Daniel Dunlavy, Eric T. Phipps, (2006). Periodic Orbits with 4D Trilinos Users Group meeting https://www.osti.gov/search/identifier:1264653 Document ID: 5247595

Michael S. Eldred, Shannon Lee Brown, Brian M. Adams, Daniel Dunlavy, David M. Gay, Laura Painton Swiler, Anthony A. Giunta, William Eugene Hart, Jean-Paul Watson, John P. Eddy, Joshua Griffin, Patricia D. Hough, Tamara G. Kolda, Monica L Martinez-Canales, Pamela J. Williams, (2006). DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis. Version 4.0 Users Manual https://www.osti.gov/search/identifier:895703 Document ID: 5246487

Shannon Lee Brown, Brian M. Adams, Daniel Dunlavy, David M. Gay, Laura Painton Swiler, Anthony A. Giunta, William Eugene Hart, Jean-Paul Watson, John P. Eddy, Joshua Griffin, Patricia D. Hough, Tamara G. Kolda, Monica L Martinez-Canales, Pamela J. Williams, (2006). DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis. Version 4.0 Developers Manual. https://www.osti.gov/search/identifier:896280 Document ID: 5243827

Shannon Lee Brown, Brian M. Adams, Daniel Dunlavy, David M. Gay, Laura Painton Swiler, Anthony A. Giunta, William Eugene Hart, Jean-Paul Watson, John P. Eddy, Joshua Griffin, Patricia D. Hough, Tamara G. Kolda, Monica L Martinez-Canales, Pamela J. Williams, (2006). DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis. Version 4.0 Reference Manual. https://www.osti.gov/search/identifier:895073 Document ID: 5243828

Tamara G. Kolda, Daniel Dunlavy, William Philip Kegelmeyer, (2006). Multilinear algebra for analyzing data with multiple linkages https://www.osti.gov/search/identifier:923081 Document ID: 5241598

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