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Center for Computing Research

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Publication	Type	Year
Overview of ML/AI at Sandia BNL workshop on AI/ML for Safeguards Michael Reed Smith, Nancy Kay Hayden, David John Stracuzzi, Nathan Shoman	Presentation (non-conference) – 2022 Presentation (non-conference)	2022
SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning Michael Reed Smith, Erin Acquesta, Arlo L. Ames, Alycia Noel Carey, Christopher Roman Cuellar, Richard V. Field, Trevor Maxfield, Scott A. Mitchell, Elizabeth Susan Morris, Blake Cameron Moss, Megan Nyre-Yu, Ahmad Rushdi, Mallory Catherine Stites, Charles Smutz, Xin Zhou https://www.osti.gov/search/identifier:1820253	SAND Report – 2021 SAND Report	2021
Going Beyond Signature Malware Detection by Learning Behaviors Malware Technical Exchange Meeting Nicholas T. Johnson, Eva Domschot, Kanad Khanna, William Philip Kegelmeyer, Christopher Lamb, Ramyaa Ramyaa, Michael Reed Smith, Stephen Joseph Verzi, Xin Zhou, Armida J. Carbajal, Bridget Haus, Joey Burton Ingram	Conference Presentation – 2021 Conference Presentation	2021
SAGE Advice? Assessing the Accuracy of ML Explanations for Model Credibility NIST AI Assurance Leadership Team Michael Reed Smith, Erin Acquesta, Richard V. Field, Trevor Maxfield, Blake Cameron Moss, Megan Nyre-Yu, Ahmad Rushdi, Charles Smutz, Mallory Catherine Stites	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Mind the Gap: On Bridging the Semantic Gap Between Machine Learning and Malware Analysis 13th ACM Workshop on Artificial Intelligence and Security Michael Reed Smith, Nicholas T. Johnson, Joey Burton Ingram, Armida J. Carbajal, Bridget Irene Haus, Eva Domschot, Ramyaa Ramyaa, Christopher Lamb, Stephen Joseph Verzi, William Philip Kegelmeyer https://www.osti.gov/search/identifier:1831009	Conference Presentation – 2020 Conference Presentation	2020
Assessing Global Sensitivity Analysis for Credibility in Machine Learning Explainability SAMSIGlobal Sensitivity Analysis Working Group Meeting Erin Acquesta, Michael Reed Smith, Richard V. Field, Trevor Maxfield, Ahmad Rushdi https://www.osti.gov/search/identifier:1820562	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
MalGen: On Bridging the Semantic Gap between Machine Learning and Malware Analysis Sandia Machine Learning and Deep Learning Workshop Michael Reed Smith, Armida J. Carbajal, Eva Domschot, Bridget Irene Haus, Joey Burton Ingram, Nicholas T. Johnson, William Philip Kegelmeyer, Christopher Lamb, Ramyaa Ramyaa, Stephen Joseph Verzi https://www.osti.gov/search/identifier:1811972	Conference Paper – 2020 Conference Paper	2020
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis 13th ACM Workshop on Artificial Intelligence and Security Michael Reed Smith, Nicholas T. Johnson, Joey Burton Ingram, Armida J. Carbajal, Bridget Irene Haus, Eva Domschot, Ramyaa Ramyaa, Christopher Lamb, Stephen Joseph Verzi, William Philip Kegelmeyer https://www.osti.gov/search/identifier:1807188	Conference Paper – 2020 Conference Paper	2020
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security The ACM Conference on Computer and Communications Security Michael Reed Smith, Nicholas T. Johnson, Joey Burton Ingram, Armida J. Carbajal, Ramyaa Ramyaa, Evelyn Domschot, Christopher Lamb, Stephen Joseph Verzi, William Philip Kegelmeyer https://www.osti.gov/search/identifier:1782493	Conference Paper – 2020 Conference Paper	2020
Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence Frontiers in Computational Neuroscience Frances S. Chance, James Bradley Aimone, Srideep Musuvathy, Michael Reed Smith, Craig Michael Vineyard, Felix Wang https://www.osti.gov/search/identifier:1617318	Journal Article – 2020 Journal Article	2020
Neuromorphic Computing Algorithms and Architecture Research at Sandia Ccd Titans Intern Tech Talk Aaron Jamison Hill, Jonathon W. Donaldson, Fredrick H. Rothganger, Craig Michael Vineyard, David R. Follett, Pamela L. Follett, Michael Reed Smith, Stephen Joseph Verzi, William Mark Severa, Felix Wang, James Bradley Aimone, John H. Naegle, Conrad D. James https://www.osti.gov/search/identifier:1645676	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Self-updating Models with Error Remediation for Bandwidth-constrained Environments 2019 IEEE Space Computing Conference (SCC) Justin E Doak, Joey Burton Ingram, Michael Reed Smith, Craig Michael Vineyard https://www.osti.gov/search/identifier:1641249	Conference Paper – 2019 Conference Paper	2019
Self-updating learning models with error correction remediation for bandwidth- constrained environments IEEE Space Computing Conference (SCC) Michael Reed Smith, Justin E Doak, Joey Burton Ingram, Craig Michael Vineyard	Abstract – 2019 Abstract	2019
Dynamic Analysis of Executables to Detect and Characterize Malware IEEE International Conference on Machine Learning and Applications (IEEE ICMLA) Michael Reed Smith, Joey Burton Ingram, Christopher Lamb, Timothy J. Draelos, Justin E Doak, James Bradley Aimone, Conrad D. James https://www.osti.gov/search/identifier:1806881	Conference Paper – 2018 Conference Paper	2018
A Spike-Timing Neuromorphic Architecture IEEE International Conference on Rebooting Computing (ICRC 2017) Aaron Jamison Hill, Jonathon W. Donaldson, Fredrick Rothganger, Craig Michael Vineyard, David R. Follett, Pamela L. Follett, Michael Reed Smith, Stephen Joseph Verzi, William Mark Severa, Felix Wang, James Bradley Aimone, John H. Naegle, Conrad D. James https://www.osti.gov/search/identifier:1470698	Conference Paper – 2017 Conference Paper	2017
Steep Deep Spiking Networks Sandia Machine Learning/Deep Learning (MLDL) Conference Stephen Joseph Verzi, Ryan Anthony Dellana, William Mark Severa, Michael Reed Smith, Fredrick Rothganger, Conrad D. James, James Bradley Aimone, Craig Michael Vineyard https://www.osti.gov/search/identifier:1464724	Conference Paper – 2017 Conference Paper	2017
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to LSMs The 2017 International Joint Conference on Neural Networks (IJCNN 2017) Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1457873	Conference Paper – 2017 Conference Paper	2017
An Efficient Implementation of a Liquid State Machine on the Spiking Temporal Processing Unit Neuro Inspired Computational Elements Workshop Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1427932	Conference Paper – 2017 Conference Paper	2017
An Efficient Implementation of a LSM on the Spiking Temporal Processing Unit Neuro Inspired Computational Elements Workshop Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone, Michael Reed Smith, Michael Reed Smith https://www.osti.gov/search/identifier:1427931	Conference Paper – 2017 Conference Paper	2017
A neurally inspired spiking temporal processing unit computational architecture Society for Neuroscience 2016 Craig Michael Vineyard, James Bradley Aimone, Stephen Joseph Verzi, Jonathon W. Donaldson, Michael Reed Smith, Fredrick Rothganger, David R. Follet, Conrad D. James, John H. Naegle https://www.osti.gov/search/identifier:1526843	Conference Paper – 2016 Conference Paper	2016
Implementation of a Liquid State Machine with Temporal Dynamics on a Novel Spiking Neuromorphic Architecture Workshop on Neuromorphic Computing and Algorithms, WI-BIH'16 Michael Reed Smith, Aaron Jamison Hill, Kristofor David Carlson, Craig Michael Vineyard, Jonathon W. Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James Bradley Aimone https://www.osti.gov/search/identifier:1405258	Conference Paper – 2016 Conference Paper	2016
A neurally inspired spiking temporal processing unit computational architecture Society for Neuroscience 2016 meeting Craig Michael Vineyard, James Bradley Aimone, Michael Reed Smith, Stephen Joseph Verzi, Gabriel ANUOLUWAPO Popoola, Felix Wang, Jonathon W. Donaldson, Pamela Follett, David Follett, Conrad D. James, John H. Naegle	Abstract – 2016 Abstract	2016
A neurally inspired spiking temporal processing unit architecture Society for Neuroscience 2016 meeting Craig Michael Vineyard, James Bradley Aimone, Michael Reed Smith, Stephen Joseph Verzi, Gabriel ANUOLUWAPO Popoola, Felix Wang, Jonathon W. Donaldson, Pamela Follett, David Follett, Conrad D. James, John H. Naegle	Abstract – 2016 Abstract	2016
Document Title	Type	Year

Publications

Overview of ML/AI at Sandia

SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning

Going Beyond Signature Malware Detection by Learning Behaviors

SAGE Advice? Assessing the Accuracy of ML Explanations for Model Credibility

Mind the Gap: On Bridging the Semantic Gap Between Machine Learning and Malware Analysis

Assessing Global Sensitivity Analysis for Credibility in Machine Learning Explainability

MalGen: On Bridging the Semantic Gap between Machine Learning and Malware Analysis

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security

Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence

Neuromorphic Computing Algorithms and Architecture Research at Sandia

Self-updating Models with Error Remediation for Bandwidth-constrained Environments

Self-updating learning models with error correction remediation for bandwidth- constrained environments

Dynamic Analysis of Executables to Detect and Characterize Malware

A Spike-Timing Neuromorphic Architecture

Steep Deep Spiking Networks

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to LSMs

An Efficient Implementation of a Liquid State Machine on the Spiking Temporal Processing Unit

An Efficient Implementation of a LSM on the Spiking Temporal Processing Unit

A neurally inspired spiking temporal processing unit computational architecture

Implementation of a Liquid State Machine with Temporal Dynamics on a Novel Spiking Neuromorphic Architecture

A neurally inspired spiking temporal processing unit computational architecture

A neurally inspired spiking temporal processing unit architecture