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Publication	Type	Year
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
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
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
In-Situ Machine Learning for Intelligent Data Capture on Exascale Platforms Artificial Intelligence for Robust Engineering & Science Warren Leon Davis IV, Timothy Malcolm Shead, Hemanth Kolla, Kevin Reed, William Philip Kegelmeyer, Gabriel ANUOLUWAPO Popoola https://www.osti.gov/search/identifier:1762271	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
The Potential of Integrated Machine Learning Algorithms for Tropical Cyclone Detection in Advanced Climate Modeling American Geophysical Union Fall Conference 2019 Warren Leon Davis IV, Timothy M. Shead, Hemanth Kolla, Gabriel ANUOLUWAPO Popoola, William Philip Kegelmeyer, Aditya Konduri https://www.osti.gov/search/identifier:1643419	Conference Paper – 2019 Conference Paper	2019
Data Analytics are Powerful -- Handle with Care Chesapeake Large-Scale Analytics Conference Jeremy D Wendt, William Philip Kegelmeyer, Ali Pinar, Timothy Malcolm Shead, Gary Joseph Saavedra, Cosmin Safta, Joseph Bertino https://www.osti.gov/search/identifier:1642746	Conference Paper – 2019 Conference Paper	2019
In-Situ Machine Learning for Intelligent Data Capture on Exascale Platforms 2019 Cis Erb Warren Leon Davis, Timothy Malcolm Shead, Hemanth Kolla, William Philip Kegelmeyer, Gabriel ANUOLUWAPO Popoola, Kevin Reed https://www.osti.gov/search/identifier:1645860	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
The Potential of In-Situ Machine Learning Algorithms for Tropical Cyclone Detection in Advanced Climate Modeling American Geophysical Union Fall Meeting 2019 Warren Leon Davis, Kevin Reed, Timothy Malcolm Shead, Hemanth Kolla, Gabriel ANUOLUWAPO Popoola, William Philip Kegelmeyer, Aditya Konduri, Julia Ling	Abstract – 2019 Abstract	2019
A Framework for In-Situ Anomaly Detection in HPC Environments Ldav 2019 Timothy Malcolm Shead, Daniel Dunlavy, Hemanth Kolla, Aditya Konduri, Gabriel ANUOLUWAPO Popoola, Warren Leon Davis, William Philip Kegelmeyer, Kevin Reed, Julia Ling https://www.osti.gov/search/identifier:1640853	Conference Paper – 2019 Conference Paper	2019
Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report Randolph Brost, Erin E. Carrier, Michelle Carroll, Katrina M. Groth, William Philip Kegelmeyer, Vitus J. Leung, Hamilton E. Link, Andrew John Patterson, Cynthia Ann Phillips, Samuel Richter, David G. Robinson, Andrea Staid, Diane M.-K. Woodbridge https://www.osti.gov/search/identifier:1530166	SAND Report – 2018 SAND Report	2018
Embedding Python for In-Situ Analysis Daniel Dunlavy, Timothy Malcolm Shead, Daniel Dunlavy, Aditya Konduri, Hemanth Kolla, Daniel Dunlavy, William Philip Kegelmeyer, Warren Leon Davis https://www.osti.gov/search/identifier:1734473	SAND Report – 2018 SAND Report	2018
Anomaly detection in scientific data using joint statistical ArXiv Aditya Konduri, Hemanth Kolla, William Philip Kegelmeyer, Timothy Malcolm Shead, Julia Ling, Warren Leon Davis https://www.osti.gov/search/identifier:1502456	Journal Article – 2018 Journal Article	2018
In-Situ Machine Learning for Intelligent Data Capture in HPC Simulations CIS External Advisory Board Meeting Warren Leon Davis, Daniel Dunlavy, William Philip Kegelmeyer, Hemanth Kolla, Aditya Konduri, Timothy Malcolm Shead, Kevin Reed https://www.osti.gov/search/identifier:1524824	Conference Paper – 2018 Conference Paper	2018
Event Detection In Multi-variate Scientific Simulations Using Feature Anomaly Metrics SIAM Conference on Parallel Processing for Scientific Computing (PP18) Aditya Konduri, Hemanth Kolla, Julia Ling, William Philip Kegelmeyer, Daniel Dunlavy, Timothy Malcolm Shead, Warren Leon Davis https://www.osti.gov/search/identifier:1499084	Conference Paper – 2018 Conference Paper	2018
Embedding Python for In-Situ Analysis In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization Timothy Malcolm Shead, Aditya Konduri, Hemanth Kolla, Daniel Dunlavy, William Philip Kegelmeyer https://www.osti.gov/search/identifier:1467787	Conference Paper – 2017 Conference Paper	2017
Using Feature Importance Metrics to Detect Events of Interest in Scientific Computing Applications Kdd 2017 Julia Ling, William Philip Kegelmeyer, Aditya Konduri, Hemanth Kolla, Kevin Reed, Timothy Malcolm Shead, Warren Leon Davis https://www.osti.gov/search/identifier:1505710	Conference Paper – 2017 Conference Paper	2017
PANTHER: Trajectory Analysis Mark Daniel Rintoul, Andrew T. Wilson, Christopher G. Valicka, William Philip Kegelmeyer, Timothy Malcolm Shead, Kristina Rodriguez Czuchlewski, Benjamin D. Newton https://www.osti.gov/search/identifier:1221864	SAND Report – 2015 SAND Report	2015
Characterizing and Detecting Aircraft Identity and Diversion The 19th Annual Signal & Image Sciences Workshop William Philip Kegelmeyer, Timothy Malcolm Shead, Mark Daniel Rintoul, Andrew T. Wilson https://www.osti.gov/search/identifier:1251131	Conference Paper – 2015 Conference Paper	2015
Machine Learning in the Presence of Adversarial Tampering, or, What To Do When Your Truth Data Lies? ASCR Machine Learning for Scientific Discovery Workshop William Philip Kegelmeyer, Timothy Malcolm Shead, Jonathan Crussell, David G. Robinson	Abstract – 2014 Abstract	2014
Advanced ALE Remeshing Methods in ALEGRA CIS External Review William Philip Kegelmeyer, Thomas E. Voth	Other Publication – 2013 Other Publication	2013
Multilinear algebra for analyzing data with multiple linkages DOE Applied Mathematics Principal Investigators Meeting Daniel Dunlavy, Tamara G. Kolda, William Philip Kegelmeyer https://www.osti.gov/search/identifier:966593	Conference Paper – 2009 Conference Paper	2009
Tensor Decompositions for Analyzing Multi-link Graphs SIAM Conference on Parallel Processing for Scientific Computing Daniel Dunlavy, Tamara G. Kolda, William Philip Kegelmeyer https://www.osti.gov/search/identifier:1145612	Conference Paper – 2008 Conference Paper	2008
Multilinear algebra for analyzing data with multiple linkages Tamara G. Kolda, Daniel Dunlavy, William Philip Kegelmeyer https://www.osti.gov/search/identifier:923081	SAND Report – 2006 SAND Report	2006
Document Title	Type	Year

Results 1–25 of 26

Publications

Going Beyond Signature Malware Detection by Learning Behaviors

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

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

In-Situ Machine Learning for Intelligent Data Capture on Exascale Platforms

The Potential of Integrated Machine Learning Algorithms for Tropical Cyclone Detection in Advanced Climate Modeling

Data Analytics are Powerful -- Handle with Care

In-Situ Machine Learning for Intelligent Data Capture on Exascale Platforms

The Potential of In-Situ Machine Learning Algorithms for Tropical Cyclone Detection in Advanced Climate Modeling

A Framework for In-Situ Anomaly Detection in HPC Environments

Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report

Embedding Python for In-Situ Analysis

Anomaly detection in scientific data using joint statistical

In-Situ Machine Learning for Intelligent Data Capture in HPC Simulations

Event Detection In Multi-variate Scientific Simulations Using Feature Anomaly Metrics

Embedding Python for In-Situ Analysis

Using Feature Importance Metrics to Detect Events of Interest in Scientific Computing Applications

PANTHER: Trajectory Analysis

Characterizing and Detecting Aircraft Identity and Diversion

Machine Learning in the Presence of Adversarial Tampering, or, What To Do When Your Truth Data Lies?

Advanced ALE Remeshing Methods in ALEGRA

Multilinear algebra for analyzing data with multiple linkages

Tensor Decompositions for Analyzing Multi-link Graphs

Multilinear algebra for analyzing data with multiple linkages