##### (*) = Invited Talks

**Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications**, Stephen L. Olivier, Nathan D. Ellingwood, Jonathan Berry, Daniel M. Dunlavy, *Chesapeake Large-Scale Analytics Conference (CLSAC’21)*, ONLINE, October 2021.

**Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications**, Stephen L. Olivier, Nathan D. Ellingwood, Jonathan Berry, Daniel M. Dunlavy, *IEEE High Performance Extreme Computing Conference (HPEC’21)*, ONLINE, September 2021.

**Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency**, Jeremy Myers, Daniel M. Dunlavy, *IEEE High Performance Extreme Computing Conference (HPEC’21)*, ONLINE, September 2021.

**Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures**, S. Isaac Geronimo Anderson, Keita Teranishi, Daniel M. Dunlavy, Jee Choi, *IEEE High Performance Extreme Computing Conference (HPEC’21)*, ONLINE, September 2021.

**Questa High Performance Data Analytics**, Daniel M. Dunlavy, Jonathan Berry, S*andia Computing and Information Sciences External Review Board*, ONLINE, March 2021.

**SparTen: Leveraging Kokkos for On-node Parallelism in a Second Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data**, Keita Teranishi, Daniel M. Dunlavy, Jeremy Myers, Richard F. Barrett, *IEEE High Performance Extreme Computing Conference (HPEC’20)*, ONLINE, September 2020.

**Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software**, Jeremy Myers, Daniel M. Dunlavy, Keita Teranishi, David S Hollman, *IEEE High Performance Extreme Computing Conference (HPEC’20)*, ONLINE, September 2020.

**Multimodal Deep Learning for Flaw Detection in Software Programs**, Scott Heidbrink, Kathryn N. Rodhouse, Daniel M. Dunlavy, *Machine Learning and Deep Learning Workshop*, ONLINE, August 2020.

**Topic Modeling with Natural Language Processing for Identification of Nuclear Proliferation-Relevant Scientific and Technical Publications**, Jonathan Bisila, Daniel M. Dunlavy, Zoe N. Gastelum, Craig D. Ulmer, *Machine Learning and Deep Learning Workshop*, ONLINE, August 2020.

**(*) Tensor Decompositions for Analyzing Multi-Way Data**, Daniel M. Dunlavy, *CSRI Summer Seminar Series*, Albuquerque, NM, July 2020.

**Topic Modeling with Natural Language Processing for Identification of Nuclear Proliferation-Relevant Scientific and Technical Publications**, Jonathan Bisila, Daniel M. Dunlavy, Zoe N. Gastelum, Craig D. Ulmer, *INMM Annual Meeting*, ONLINE, July 2020.

**Performance and Parallelization of CP-Alternate Poisson Regression Sparse Tensor Decomposition**, Keita Teranishi, D.S. Hollman, Jeremy Myers, Daniel M. Dunlavy, *SIAM Conference on Parallel Processing for Scientific Computing*, Seattle, WA, February 2020.

**Detecting Flaws in Software Programs using Multimodal Deep Learning**, Daniel M. Dunlavy, Kathryn N. Rodhouse, Scott Heidbrink, *Conference on Data Analysis*, Santa Fe, NM, February 2020.

**Natural Language Processing for Topic Identification supporting Document Search and Identification for Nuclear Proliferation Detection**, Daniel M. Dunlavy, Jonathan Bisila, Zoe N. Gastelum, *Conference on Data Analysis*, Santa Fe, NM, February 2020.

**Performance and Parallelization of CP-Alternate Poisson Regression Sparse Tensor Decomposition**, Keita Teranishi, Richard F. Barrett, D.S. Hollman, Jeremy Myers, Daniel M. Dunlavy, *AI and Tensor Factorizations for Physical, Chemical and Biological Systems*, Santa Fe, NM, September 2019.

**Applying Natural Language Processing (NLP) to RF Signal Patterns**, Wesley Alexander Brooks, Daniel M. Dunlavy, Jennifer Galasso, Christopher M. Howerter, Nicholas Jacobs, Christine F. Lai, *Machine Learning and Deep Learning Workshop*, Albuquerque, NM, August 2019.

**Development of Parallel Sparse CP-APR Tensor Decomposition Solvers**, Keita Teranishi, Richard F. Barrett, D.S. Hollman, Daniel M. Dunlavy, *Kokkos User Group Meeting*, Albuquerque, NM, April 2019.

**Performance Portable Parallel Sparse CP-APR Tensor Decompositions**, Keita Teranishi, Richard F. Barrett, Christopher J. Forster, Tamara G. Kolda, Daniel M. Dunlavy, *SIAM Conference on Computational Science and Engineering*, Spokane, WA, February 2019.

**Exploiting Scientific Software to Solve Problems in Data Analytics**, Karen D. Devine, Erik Boman, Tamara G. Kolda, Michael Wolf, Daniel M. Dunlavy, *IPAM Workshop III: HPC for Computationally and Data-Intensive Problems*, Los Angeles, CA, November 2018.

**Static Source Code Analysis Using Neural Networks**, Gary J. Saavedra, Daniel M. Dunlavy, *Machine Learning and Deep Learning Workshop*, Albuquerque, NM, July 2018.

**A Performance Portable Parallel CP-APR Decomposition**, Keita Teranishi, Richard F. Barrett, Christopher J. Forster, Tamara G. Kolda, Daniel M. Dunlavy, *Sparse Days*, Toulouse, France, September 2018.

**Scalable Tensor Factorizations on Multiple Architectures**, Daniel M. Dunlavy, Christopher J. Forster, Keita Teranishi, Tamara G. Kolda, *Three-way Methods in Chemistry and Psychology (TRICAP)*, Angel Fire, NM, June 2018.

**Analysis of Performance and Portability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures using Kokkos**, Christopher J. Forster, Keita Teranishi, Tamara G. Kolda, Daniel M. Dunlavy, *NVIDIA GPU Technology Conference*, San Jose, CA, March 2018.

**Event Detection in Multivariate Scientific Simulations using Feature Anomaly Metrics**, Aditya Konduri, Hemanth Kolla, Julia Ling, W. Philip Kegelmeyer, Daniel M. Dunlavy, Timothy M. Shead, Warren L. Davis IV, *SIAM Conference on Parallel Computing*, Tokyo, Japan, March 2018.

**Analysis of Performance and Portability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures**, Christopher J. Forster, Keita Teranishi, Tamara G. Kolda, Daniel M. Dunlavy*, SIAM Conference on Parallel Computing*, Tokyo, Japan, March 2018.

**TriData: High Performance Linear Algebra-Based Data Analytics**, Michael Wolf, Richard B. Lehoucq, Daniel Bourgeois, Alicia M. Klinvex, Daniel M. Dunlavy, *SIAM Conference on Parallel Computing*, Tokyo, Japan, March 2018.

**Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures**, Keita Teranishi, Christopher J. Forster, Tamara G. Kolda, Daniel M. Dunlavy, *Advanced Supercomputing Environment (ASE) Seminar*, Tokyo, Japan, August 2017.

**Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures**, Christopher J. Forster, Keita Teranishi, Greg E. Mackey, Tamara G. Kolda, Daniel M. Dunlavy, *SIAM Annual Meeting, Pittsburgh*, PA, July 2017.

**Portability and Scalability of Sparse Tensor Decompositions on CPU/MIC/GPU Architectures**, Daniel M. Dunlavy,

*HPC Analytics Workshop*, Hanover, MD, June 2017.

**A Computational Spectral Graph Theory Tutorial**, Richard B. Lehoucq, Erik G. Boman, Karen D. Devine, Jonathan W. Berry, Michael Wolf, Van Henson, Geoff Sanders, Daniel M. Dunlavy, *Householder Symposium XX*, Blacksburg, VA, June 2017.

**TriData: Applying Trilinos to Data Analysis**, Michael Wolf, Alicia M. Klinvex, Daniel M. Dunlavy, *Trilinos User-Developer Group Meeting*, Albuquerque, NM, October 2016.

**Hypergraph Exploitation for Data Sciences**, Michael Wolf, Alicia M. Klinvex, Daniel M. Dunlavy, *Graph Exploitation Symposium*, Dedham, MA, May 2016.

**Clustering Network Data through Effective Use of Eigensolvers and Hypergraph Models**, Alicia M. Klinvex, Michael Wolf, Daniel M. Dunlavy, *Copper Mountain Conference on Iterative Methods*, Copper, CO, March 2016.

**(*) Clustering Network Data using Graphs, Hypergraphs, and Tensors**, Daniel M. Dunlavy, *Statistical and Computational Challenges in Networks and Cybersecurity*, Montreal, Canada, May 2015.

**(*) Data Analysis using Tensor Factorizations**, Daniel M. Dunlavy, *CSRI Summer Seminar Series*, Albuquerque, NM, July 2013.

**Community Detection: A Bayesian Approach and the Challenge of Evaluation**, Jonathan W. Berry, Daniel M. Dunlavy, Cynthia A. Phillips, David G. Robinson, Jiqiang Guo, Daniel Nordman, Alyson Wilson, *Structure, Statistical Inference, and Dynamics Networks: From Graphs to Rich Data*, Santa Fe, NM, May 2013.

**Community Detection: A Bayesian Approach and the Challenge of Evaluation**, Jonathan W. Berry, Daniel M. Dunlavy, Cynthia A. Phillips, David G. Robinson, Jiqiang Guo, Daniel Nordman, Alyson Wilson, *Graph Exploitation Workshop*, Dedham, MA, April 2012.

**Parallel Bayesian Methods for Community Detection**, Jonathan W. Berry, Daniel M. Dunlavy, Cynthia A. Phillips, David G. Robinson, Jiqiang Guo, Daniel Nordman, Alyson Wilson, *SIAM Conference on Parallel Computing*, Savannah, GA, February 2012.

**(*) Text Analysis, Factor Modeling and Machine Learning**, Daniel M. Dunlavy, *EECS Department Seminar*, Northwestern University, Evanston, IL, November 2011.

**Scalable Tensor Factorizations with Incomplete Data**, Tamara G. Kolda, Evrim Acar Ataman, Daniel M. Dunlavy, Morten Mørup, *SIAM Annual Meeting*, Pittsburgh, PA, July 2010.

**Compressively Sensed Complex Networks**, Jaideepp Ray, Ali Pinar, Daniel M. Dunlavy, *SIAM Annual Meeting*, Pittsburgh, PA, July 2010.

**ParaText: Scalable Text Analysis and Visualization**, Daniel M. Dunlavy, Eric T. Stanton, Timothy M. Shead, *SIAM Annual Meeting*, Pittsburgh, PA, July 2010.

**Link Prediction on Evolving Graphs using Matrix and Tensor Factorizations**, Tamara G. Kolda, Evrim Acar Ataman, Daniel M. Dunlavy, *BIT 50 – Trends in Numerical Computing*, Lund, Sweden, June 2010.

**ParaText: Scalable Text Analysis and Visualization**, Daniel M. Dunlavy, Eric T. Stanton, Timothy M. Shead, *SIAM Conference on Parallel Processing for Scientific Computing*, Seattle, WA, February 2010.

**Scalable Tensor Factorizations with Incomplete Data**, Tamara G. Kolda, Evrim Acar Ataman, Daniel M. Dunlavy, Morten Mørup, *Sparse Random Structures: Analysis and Computation*, Banff, AB, Canada, January 2010.

**Coupled Tensor and Matrix Factorizations**, Tamara G. Kolda, Evrim Acar Ataman, Daniel M. Dunlavy, *SIAM Conference on Applied Linear Algebra*, Monterey Bay-Seaside, CA, October 2009.

**Scalable Tensor Factorizations with Missing Data**, Evrim Acar Ataman, Tamara G. Kolda, Daniel M. Dunlavy, *SIAM Conference on Applied Linear Algebra*, Monterey Bay-Seaside, CA, October 2009.

**Persistent Homology for Parameter Sensitivity in Large-scale Text-analysis (Informatics) Graphs**, Daniel M. Dunlavy, *CSRI Workshop on Combinatorial Algebraic Topology (CAT)*, Santa Fe, NM, August 2009.

**ISE Informatics: Statistical Analysis of Heterogeneous Data for Test Quality Assurance**, Brett W. Bader, Mark Van Benthem, Shawn Martin, Daniel M. Dunlavy, *Compatibility, Aging & Stockpile Stewardship Conference*, Albuquerque, NM, September 2009.

**Scalable Solutions for Processing and Searching Very Large Document Collections**, Daniel M. Dunlavy, Eric T. Stanton, Timothy M. Shead, *NNSA LDRD Symposium*, Washington, D.C., August 2009.

**An Optimization Approach for Fitting Canonical Tensor Decompositions**, Daniel M. Dunlavy, Evrim Acar Ataman, Tamara G. Kolda, *SIAM Annual Meeting*, Denver, CO, July 2009.

**An Optimization Approach for Fitting a CANDECOMP/PARAFAC Model with Applications in Social Network Analysis**, Evrim Acar Ataman, Tamara G. Kolda, Daniel M. Dunlavy, *Three-way Methods in Chemistry and Psychology (TRICAP)*, Vall de Nuria, Spain, June 2009.

**The Canonical Tensor Decomposition and Its Applications to Data Analysis**, Evrim Acar Ataman, Tamara G. Kolda, Daniel M. Dunlavy, *Linear Algebra and Optimization Seminar*, Palo Alto, CA, May 2009.

**Link Prediction on Evolving Data using Tensor Factorizations**, Evrim Acar Ataman, Tamara G. Kolda, Daniel M. Dunlavy, *SIAM Conference on Computational Science and Engineering*, Miami, FL, March 2009.

**Coupled Matrix Factorizations Using Optimization**, Daniel M. Dunlavy, Evrim Acar Ataman, Tamara G. Kolda, *SIAM Conference on Computation Science and Engineering*, Miami, FL, March 2009.

**ParaText: Leveraging Scalable Scientific Computing Capabilities for Large-Scale Text Analysis and Visualization**, Daniel M. Dunlavy, Eric T. Stanton, Timothy M. Shead, *SIAM Conference on Computation Science and Engineering*, Miami, FL, March 2009.

**CPOPT: Optimization for Fitting CANDECOMP/PARAFAC Models**, Tamara G. Kolda, Evrim Acar Ataman, Daniel M. Dunlavy, *Computational Algebraic Statistics, Theories and Applications (CASTA2008)*, Kyoto, Japan, December 2008.

**CPOPT: Optimization for Fitting CANDECOMP/PARAFAC Models**, Tamara G. Kolda, Evrim Acar Ataman, Daniel M. Dunlavy, *Multi-Manifold Data Modeling and Applications*, Minneapolis, MN, October 2008.

**Multilinear Algebra for Analyzing Data with Multiple Linkages**, Daniel M. Dunlavy, Tamara G. Kolda, W. Philip Kegelmeyer,* DOE Applied Mathematics Principal Investigators Meeting*, Argonne, IL, October 2008.

**(*) Text Analysis: Methods for Searching, Organizing, Labeling and Summarizing Document Collections**, Daniel M. Dunlavy, *CSRI Student Seminar Series*, Albuquerque, NM, July 2008.

**Heterogeneous Ensemble Classification**, Daniel M. Dunlavy, Sean A. Gilpin, *Sandia Workshop on Data Mining and Data Analysis*, Albuquerque, NM, July 2008.

**Coupling Informatics Algorithm Development and Visual Analysis**, Daniel M. Dunlavy, *SIAM Annual Meeting*, San Diego, CA, July 2008.

**Tensor Decompositions for Analyzing Multi-link Graphs**, Daniel M. Dunlavy, Tamara G. Kolda, W. Philip Kegelmeyer, *SIAM Conference on Parallel Processing for Scientific Computing*, Atlanta, GA, March 2008.

**The Titan Project: Requirements for Data Mining & Analysis,**, Daniel M. Dunlavy, Timothy M. Shead, Patricia J. Crossno, Eric T. Stanton, *Trilinos User Group Meeting*, Albuquerque, NM, November 2007.

**(*) Multilinear Algebra for Analyzing Data with Multiple Linkages**, Daniel M. Dunlavy, Tamara G. Kolda, W. Philip Kegelmeyer, *Department of Mathematics Colloquium*, Western Michigan University, Kalamazoo, MI, September 2007.

**(*) Applying Mathematics: Solving Problems at a National Laboratory**, Daniel M. Dunlavy, *Pi Mu Epsilon Talk*, Western Michigan University, Kalamazoo, MI, September 2007.

**(*) Planning for a Successful Career at a National Laboratory**, Daniel M. Dunlavy, *Graduation Conference*, University of Maryland, College Park, MD, April 2007.

**(*) The Use of Homotopies and Continuation in Solving Challenging Problems in the Physical and Life Sciences**, Daniel M. Dunlavy, *Department of Mathematics and Computer Science Colloquium*, Chicago State University, Chicago, IL, March 2007.

**Formulations for Surrogate-Based Optimization with Data Fit and Multifidelity Models**, Daniel M. Dunlavy, Michael S. Eldred, *SIAM Conference on Computational Science and Engineering*, Costa Mesa, CA, February 2007.

**Periodic Orbits with 4D**, Andrew G. Salinger, Eric T. Phipps, Daniel M. Dunlavy, *Trilinos Users Group Meeting*, Albuquerque, NM, November 2006.

**Global Optimization: For Some Problems, There’s HOPE**, Daniel M. Dunlavy, Dianne P. O’Leary, *Ninth Copper Mountain Conference on Iterative Methods*, Copper Mountain, CO, April 2006.

**Preconditioners for the Space-Time Solution of Large-Scale PDE Applications**, Daniel M. Dunlavy, Andrew G. Salinger, *SIAM Conference on Parallel Processing*, San Francisco, CA, February 2006.

**(*) Global Optimization: For Some Problems, There’s HOPE**, Daniel M. Dunlavy, Dianne P. O’Leary, *Argonne National Laboratory*, Argonne, IL, March 2005.

**(*) Global Optimization: For Some Problems, There’s HOPE**, Daniel M. Dunlavy, Dianne P. O’Leary, *Sandia National Laboratories*, Livermore, CA, March 2005.

** A Homotopy Method for Predicting Low Energy Conformations of Proteins**, Daniel M. Dunlavy, Dianne P. O’Leary, *SIAM Conference on Computational Science and Engineering*, Orlando, FL, February 2005.

**A Homotopy Method for Finding Low Energy Conformations of Polypeptides**, Daniel M. Dunlavy, *SIAM Conference on the Life Sciences*, Portland, OR, July 2004.

**Structure Preserving Eigensolvers**, Daniel M. Dunlavy, Niloufer Mackey, D. Steven Mackey, *SIAM Applied Linear Algebra Meeting*, Williamsburg, VA, July 2003.

**(*) A Homotopy Method for Predicting the State of Minimal Energy for Chains of Charged Particles**, Daniel M. Dunlavy, *Spotlight on Graduate Research Winner’s Lecture*, Department of Mathematics, University of Maryland, February 2003.

**A Homotopy Method for Predicting the State of Minimal Energy for Chains of Charged Particles**, Daniel M. Dunlavy,* Graduate Research Interaction Day*, University of Maryland, April 2002.

**Mathematical Modeling in Industry: Notes from a Graduate Workshop**, Daniel M. Dunlavy, *Pi Mu Epsilon Colloquium Series*, Western Michigan University, January 2001.