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Publication	Type	Year
But it Looks so Real! Challenges in Training Models with Synthetic Data for International Safeguards INMM annual meeting Zoe Nellie Gastelum, Timothy Malcolm Shead, Matthew Randolph Marshall	Conference Paper – 2022 Conference Paper	2022
Deep Deception Artificial intelligence and machine learning for IAEA safeguards David Rushton Farley, Zoe Nellie Gastelum, Timothy Malcolm Shead	Conference Presentation – 2022 Conference Presentation	2022
But it Looks so Real! Challenges in Training Models with Synthetic Data for International Safeguards Institute of Nuclear Materials Management Annual Meeting Zoe Nellie Gastelum, Timothy Malcolm Shead	Abstract – 2022 Abstract	2022
Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks Jonathan W. Berry, Anand Ganti, Kenneth Goss, Carolyn Doo Mayer, Uzoma Onunkwo, Cynthia Ann Phillips, Jared Saia, Timothy Malcolm Shead https://www.osti.gov/search/identifier:1842271	SAND Report – 2022 SAND Report	2022
Machine Learning Classification for Rapid CAD-to-Simulation SIAM International Meshing Roundtable Steven J. Owen, Armida J. Carbajal, Corey Devan Ernst, Matthew Gregor Peterson, Timothy Malcolm Shead	Conference Paper – 2022 Conference Paper	2022
A Safeguards-Informed Image Dataset for Computer Vision R&D INMM/ESARDA Joint Annual Meeting Zoe Nellie Gastelum, Timothy Malcolm Shead, Ahmad Rushdi	Conference Presentation – 2021 Conference Presentation	2021
Synthetic Images for Machine Learning CGI for Science Workshop Timothy Malcolm Shead, Zoe Nellie Gastelum, Ahmad Rushdi	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
A Large Safeguards-Informed Hybrid Imagery Dataset for Computer Vision Research and Development Joint Annual INMM/ESARDA Meeeting Zoe Nellie Gastelum, Timothy Malcolm Shead, Ahmad Rushdi	Conference Paper – 2021 Conference Paper	2021
Safeguards-Informed Hybrid Imagery Dataset Nuclear Security Applications Research & Development Program Review Meeting Joshua Edward Rutkowski, Zoe Nellie Gastelum, Timothy Malcolm Shead, Ahmad Rushdi, Jason C. Bolles, Arielle Mattes	Conference Poster – 2021 Conference Poster	2021
A Large Safeguards-Informed Hybrid Imagery Dataset for Computer Vision Research and Development INMM/ESARDA Joint Annual Meeting Zoe Nellie Gastelum, Timothy Malcolm Shead, Ahmad Rushdi	Abstract – 2021 Abstract	2021
How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection Zoe Nellie Gastelum, Timothy Malcolm Shead https://www.osti.gov/search/identifier:1670874	SAND Report – 2020 SAND Report	2020
Synthetic Training Images for Real-World Object Detection INMM Annual Meeting Zoe Nellie Gastelum, Timothy Malcolm Shead https://www.osti.gov/search/identifier:1804625	Conference Paper – 2020 Conference Paper	2020
Synthetic Training Images for Real-World Object Detection INMM Annual Meeting Zoe Nellie Gastelum, Timothy Malcolm Shead, Michael Higgins https://www.osti.gov/search/identifier:1807426	Conference Paper – 2020 Conference Paper	2020
The Use of Synthetic 3D Images to Drastically Reduce Real-World Training Data for Object Detection Models Conference on Data Analysis Zoe Nellie Gastelum, Timothy Malcolm Shead, Michael Higgins https://www.osti.gov/search/identifier:1766905	Conference Paper – 2020 Conference Paper	2020
Synthetic Training Images for Real-World Object Detection INMM Annual Meeting Zoe Nellie Gastelum, Timothy Malcolm Shead, Michael Higgins	Abstract – 2020 Abstract	2020
Using Unsupervised Anomaly Detection for Data Reduction in Temporal and Spatial Data BioXFEL 2020 Gabriel ANUOLUWAPO Popoola, Warren Leon Davis, Timothy Malcolm Shead, Corey Graves https://www.osti.gov/search/identifier:1763159	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 Use of Synthetic 3D Images to Drastically Reduce Real-World Training Data for Object Detection Models Conference on Data Analysis Zoe Nellie Gastelum, Timothy Malcolm Shead, Michael Higgins	Abstract – 2020 Abstract	2020
Information-Theoretically Secure Distributed Machine Learning Timothy Malcolm Shead, Jonathan W. Berry, Cynthia Ann Phillips, Jared Saia https://www.osti.gov/search/identifier:1763277	SAND Report – 2019 SAND Report	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
A Framework for In-Situ Anomaly Detection in HPC Environments Supercomputing 2019 Timothy Malcolm Shead, Hemanth Kolla, Aditya Konduri, Gabriel Papoola, Warren Leon Davis, Daniel Dunlavy, Kevin Reed https://www.osti.gov/search/identifier:1642180	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
Integrating Physical and Informational Sensing to Support Nonproliferation Assessments of Nuclear-Related Facilities INMM Annual Meeting Zoe Nellie Gastelum, Bethany (UCB) Goldblum, Timothy Malcolm Shead, Christopher (UCB) Stewart, Aaron (PNNL) Luttman https://www.osti.gov/search/identifier:1641009	Conference Paper – 2019 Conference Paper	2019
CAD Defeaturing using Machine Learning International Meshing Roundtable Steven J. Owen, Timothy Malcolm Shead, Shawn Martin https://www.osti.gov/search/identifier:1640901	Conference Paper – 2019 Conference Paper	2019
Document Title	Type	Year

Results 1–25 of 66

Publications

But it Looks so Real! Challenges in Training Models with Synthetic Data for International Safeguards

Deep Deception

But it Looks so Real! Challenges in Training Models with Synthetic Data for International Safeguards

Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks

Machine Learning Classification for Rapid CAD-to-Simulation

A Safeguards-Informed Image Dataset for Computer Vision R&D

Synthetic Images for Machine Learning

A Large Safeguards-Informed Hybrid Imagery Dataset for Computer Vision Research and Development

Safeguards-Informed Hybrid Imagery Dataset

A Large Safeguards-Informed Hybrid Imagery Dataset for Computer Vision Research and Development

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Synthetic Training Images for Real-World Object Detection

Synthetic Training Images for Real-World Object Detection

The Use of Synthetic 3D Images to Drastically Reduce Real-World Training Data for Object Detection Models

Synthetic Training Images for Real-World Object Detection

Using Unsupervised Anomaly Detection for Data Reduction in Temporal and Spatial Data

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

The Use of Synthetic 3D Images to Drastically Reduce Real-World Training Data for Object Detection Models

Information-Theoretically Secure Distributed Machine Learning

Data Analytics are Powerful -- Handle with Care

A Framework for In-Situ Anomaly Detection in HPC Environments

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

Integrating Physical and Informational Sensing to Support Nonproliferation Assessments of Nuclear-Related Facilities

CAD Defeaturing using Machine Learning