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Publication	Type	Year
Robust initializations of variational inference with Gaussian mixtures through global optimization and Laplace approximations SIAM Conference on Uncertainty Quantification Wyatt Haynes Bridgman, Ahmad Rushdi, Mohammad Khalil	Abstract – 2022 Abstract	2022
Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics AIAA SciTech Forum Ahmad Rushdi	Conference Paper – 2021 Conference Paper	2021
The ASC Advanced Machine Learning Initiative at Sandia National Laboratories: FY21 Accomplishments and FY22 Plans ASC AMLI Program Review Ron A. Oldfield, Sharlotte LorraineBolyard Kramer, Ahmad Rushdi, Erin Acquesta, John M Emery, Paul Allen Kuberry, Jaideep Ray, Sarah Ackerman, Eric Christopher Cyr, Gary Joseph Saavedra, Clayton Hughes, Suma George Cardwell, John Darby Smith	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Multifidelity data fusion in convolutional encoder/decoder networks SIAM Conference on Uncertainty Quantification (UQ22) Lauren Partin, Gianluca Geraci, Ahmad Rushdi, Michael S. Eldred, Daniele Schiavazzi	Abstract – 2021 Abstract	2021
The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact NASA Langley HPC Seminar Series Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone, Timothy Michael Wildey, Ahmad Rushdi, Daniel Thomas Seidl	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
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
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
Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics CSRI Poster Blitz Ahmad Rushdi	Conference Poster – 2021 Conference Poster	2021
Efficient DNN Architectures for Time Series Classification Sandia MLDL Workshop Ahmad Rushdi https://www.osti.gov/search/identifier:1877803	Conference Presentation – 2021 Conference Presentation	2021
Multi-fidelity Machine Learning Machine Learning and Deep Learning Conference John Davis Jakeman, Michael S. Eldred, Gianluca Geraci, Teresa Portone, Ahmad Rushdi, Daniel Thomas Seidl, Thomas M. Smith https://www.osti.gov/search/identifier:1876608	Conference Presentation – 2021 Conference Presentation	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
Learning missing mechanisms in a dynamical system from a subset of state variable observations 16th U.S. National Congress on Computational Mechanics Teresa Portone, Erin Acquesta, Ahmad Rushdi, Raj Dandekar, Chris Rackauckas	Abstract – 2021 Abstract	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
Estimating Predictive Uncertainty in Scientific Machine Learning: A Library of Methods and Test Problems WorkshopUncertainty Management and Machine Learning in Engineering Applications Ahmad Rushdi https://www.osti.gov/search/identifier:1831758	Conference Presentation – 2020 Conference Presentation	2020
CSRI Summer Proceedings 2020 Ahmad Rushdi https://www.osti.gov/search/identifier:1716561	Report – 2020 Report	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
Estimating Predictive Uncertainty in Scientific Machine Learning: A Library of Methods and Test Problems Sandia Machine Learning and Deep Learning Workshop Ahmad Rushdi https://www.osti.gov/search/identifier:1811971	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
CSRI Virtual Poster Blitz, Intern Presentations CSRI Virtual Poster Blitz Ahmad Rushdi	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User?s Manual Brian M. Adams, William J. Bohnhoff, Keith Dalbey, Mohamed Salah Ebeida, John P. Eddy, Michael S. Eldred, Russell Hooper, Patricia D. Hough, Kenneth Hu, John Davis Jakeman, Mohammad Khalil, Kathryn Anne Maupin, Jason A. Monschke, Elliott Marshall Ridgway, Ahmad Rushdi, Daniel Thomas Seidl, John Adam Stephens, Laura Painton Swiler, Justin Winokur https://www.osti.gov/search/identifier:1630694	SAND Report – 2020 SAND Report	2020
Power Prediction using Multivariate Machine Learning in Airborne Wind Energy Systems Energies Mostafa Rushdi, Tarek Dief, Shigeo Yoshida, Roland Schmedhl, Ahmad Rushdi https://www.osti.gov/search/identifier:1644068	Journal Article – 2020 Journal Article	2020
Embracing Randomness for Uncertainty Quantification in Neural Networks Conference on Data Analysis (CoDA) 2020 Ahmad Rushdi https://www.osti.gov/search/identifier:1766912	Conference Paper – 2020 Conference Paper	2020
Rigorous Data Fusion for Computationally Expensive Simulations Nickolas Winovich, Ahmad Rushdi, Eric T. Phipps, Jaideep Ray, Guang Lin, Mohamed Salah Ebeida https://www.osti.gov/search/identifier:1560809	SAND Report – 2019 SAND Report	2019
Document Title	Type	Year

Results 1–25 of 28

Publications

Robust initializations of variational inference with Gaussian mixtures through global optimization and Laplace approximations

Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics

The ASC Advanced Machine Learning Initiative at Sandia National Laboratories: FY21 Accomplishments and FY22 Plans

Multifidelity data fusion in convolutional encoder/decoder networks

The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact

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

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

Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics

Efficient DNN Architectures for Time Series Classification

Multi-fidelity Machine Learning

Safeguards-Informed Hybrid Imagery Dataset

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

Learning missing mechanisms in a dynamical system from a subset of state variable observations

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

Estimating Predictive Uncertainty in Scientific Machine Learning: A Library of Methods and Test Problems

CSRI Summer Proceedings 2020

Assessing Global Sensitivity Analysis for Credibility in Machine Learning Explainability

Estimating Predictive Uncertainty in Scientific Machine Learning: A Library of Methods and Test Problems

CSRI Virtual Poster Blitz, Intern Presentations

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User?s Manual

Power Prediction using Multivariate Machine Learning in Airborne Wind Energy Systems

Embracing Randomness for Uncertainty Quantification in Neural Networks

Rigorous Data Fusion for Computationally Expensive Simulations