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Publication	Type	Year
Exploring Explicit Uncertainty for Binary Analysis (EUBA) Michelle A. Leger, Michael Christopher Darling, Stephen T. Jones, Laura E. Matzen, David John Stracuzzi, Andrew T. Wilson, Denis Bueno, Matthew Christentsen, Melissa Ginaldi, David Alexander Hannasch, Scott Heidbrink, Breannan Carole Howell, Chris L. Leger, Geoffrey Edward Reedy, Alisa Nicole Rogers, Jack Arthur Williams https://www.osti.gov/search/identifier:1832314	SAND Report – 2021 SAND Report	2021
Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions Artificial Intelligence and Statistics 2022 Michael Christopher Darling, Justin E Doak, Richard V. Field, Mark A. Smith, David John Stracuzzi	Abstract – 2021 Abstract	2021
Optimizing Machine Learning Predictions with Prediction Uncertainty Sandia Academic Alliance/ UT Austin Student LDRD Poster Session Zachary J. Smith, Michael Christopher Darling, David John Stracuzzi, Justin E Doak, Mark A. Smith, J. Eric Bickel https://www.osti.gov/search/identifier:1860307	Conference Poster – 2021 Conference Poster	2021
A Novel Measure of Uncertainty For Machine Learning Predictions Conference On Data Analytics (CoDA) Michael Christopher Darling, Don R. Hush, David John Stracuzzi https://www.osti.gov/search/identifier:1768156	Conference Paper – 2020 Conference Paper	2020
Using Uncertainty To Interpret Machine Learning Predictions Conference On Data Analytics Michael Christopher Darling, Don Rhea Hush, David John Stracuzzi	Abstract – 2020 Abstract	2020
Quantifying Uncertainty to Improve Decision Making in Machine Learning David John Stracuzzi, Michael Christopher Darling, Matthew Gregor Peterson, Maximillian Gene Chen https://www.osti.gov/search/identifier:1481629	SAND Report – 2018 SAND Report	2018
Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data Maximillian Gene Chen, Michael Christopher Darling, David John Stracuzzi https://www.osti.gov/search/identifier:1475256	SAND Report – 2018 SAND Report	2018
A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models Cornell Day of Statistics Maximillian Gene Chen, David John Stracuzzi, Michael Christopher Darling https://www.osti.gov/search/identifier:1591721	Conference Paper – 2018 Conference Paper	2018
A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models Joint Statistical Meetings Maximillian Gene Chen, David John Stracuzzi, Michael Christopher Darling https://www.osti.gov/search/identifier:1575046	Conference Paper – 2018 Conference Paper	2018
Using Uncertainty to Understand Machine Learning Models and Decisions Research Challenges and Opportunities at the interface of Machine Learning and Uncertainty Quantification Maximillian Gene Chen, Michael Christopher Darling, Charles Vollmer, Matthew Gregor Peterson, David John Stracuzzi https://www.osti.gov/search/identifier:1526113	Conference Paper – 2018 Conference Paper	2018
Data-Driven Uncertainty Quantification for Multisensor Analytics SPIE Defense+Security David John Stracuzzi, Michael Christopher Darling, Maximillian Gene Chen, Matthew Gregor Peterson https://www.osti.gov/search/identifier:1575173	Conference Paper – 2018 Conference Paper	2018
Data-Driven Uncertainty Quantification for Multi-Sensor Analytics SPIE Defense + Security David John Stracuzzi, Michael Christopher Darling, Maximillian Gene Chen, Matthew Gregor Peterson https://www.osti.gov/search/identifier:1507484	Conference Paper – 2018 Conference Paper	2018
Uncertainty Propagation In Multilayer Analysis Conference On Data Analytics Michael Christopher Darling, David John Stracuzzi, Maximillian Gene Chen https://www.osti.gov/search/identifier:1498629	Conference Paper – 2018 Conference Paper	2018
Multimodal Image Analysis and Uncertainty Quantification via Nonparametric Probabilistic Clustering Conference on Data Analysis 2018 Maximillian Gene Chen, Michael Christopher Darling, David John Stracuzzi https://www.osti.gov/search/identifier:1498444	Conference Paper – 2018 Conference Paper	2018
Multimodal Image Analysis and Uncertainty Quantification via Nonparametric Probabilistic Clustering Conference on Data Analysis 2018 Maximillian Gene Chen, David John Stracuzzi, Michael Christopher Darling	Abstract – 2018 Abstract	2018
Uncertainty Propagation In Multimodal Image Analysis Conference On Data Analytics Michael Christopher Darling, David John Stracuzzi, Maximillian Gene Chen	Abstract – 2018 Abstract	2018
A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models Joint Statistical Meetings 2018 Maximillian Gene Chen, David John Stracuzzi, Michael Christopher Darling	Abstract – 2018 Abstract	2018
Toward Uncertainty Quantification for Supervised Classification Michael Christopher Darling, David John Stracuzzi https://www.osti.gov/search/identifier:1527311	SAND Report – 2018 SAND Report	2018
From Data to Decisions: Placing Machine Learning Challenges In Context Neural Information Processing Systems Workshop on Challenges in Machine Learning David John Stracuzzi, Maximillian Gene Chen, Michael Christopher Darling, Matthew Gregor Peterson https://www.osti.gov/search/identifier:1484938	Conference Paper – 2017 Conference Paper	2017
Establishing Uniform Image Segmentation Ground Truth Protocols for Uncertainty Quantification and Improved Model Evaluation Machine Learning Challenges as a Research Tool Maximillian Gene Chen, David John Stracuzzi, Michael Christopher Darling, Matthew Gregor Peterson https://www.osti.gov/search/identifier:1511807	Conference Paper – 2017 Conference Paper	2017
Data-Driven Uncertainty Quantification for Multi-Sensor Analytics SPIE Defense and Security Conference David John Stracuzzi, Maximillian Gene Chen, Michael Christopher Darling, Matthew Gregor Peterson	Abstract – 2017 Abstract	2017
Establishing Uniform Image Segmentation Ground Truth Protocols for Uncertainty Quantification and Improved Model Evaluation Challenges in Machine Learning 2017 Maximillian Gene Chen, David John Stracuzzi, Michael Christopher Darling, Matthew Gregor Peterson	Abstract – 2017 Abstract	2017
Increasing Trust By Quantifying Uncertainty Trustworthy Algorithmic Decision-Making Michael Christopher Darling, David John Stracuzzi, Matthew Gregor Peterson, Maximillian Gene Chen, Michael Christopher Darling, Michael Christopher Darling	Abstract – 2017 Abstract	2017
From Data to Decisions: Placing Machine Learning Challenges in Context NIPS 2017 WorkshopMachine Learning Challenges as a Research Tool David John Stracuzzi, Maximillian Gene Chen, Michael Christopher Darling, Matthew Gregor Peterson	Abstract – 2017 Abstract	2017
Uncertainty Quantification for Machine Learning David John Stracuzzi, Maximillian Gene Chen, Michael Christopher Darling, Matthew Gregor Peterson, Charles Vollmer https://www.osti.gov/search/identifier:1733262	SAND Report – 2017 SAND Report	2017
Document Title	Type	Year

Results 1–25 of 27

Publications

Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions

Optimizing Machine Learning Predictions with Prediction Uncertainty

A Novel Measure of Uncertainty For Machine Learning Predictions

Using Uncertainty To Interpret Machine Learning Predictions

Quantifying Uncertainty to Improve Decision Making in Machine Learning

Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data

A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models

A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models

Using Uncertainty to Understand Machine Learning Models and Decisions

Data-Driven Uncertainty Quantification for Multisensor Analytics

Data-Driven Uncertainty Quantification for Multi-Sensor Analytics

Uncertainty Propagation In Multilayer Analysis

Multimodal Image Analysis and Uncertainty Quantification via Nonparametric Probabilistic Clustering

Multimodal Image Analysis and Uncertainty Quantification via Nonparametric Probabilistic Clustering

Uncertainty Propagation In Multimodal Image Analysis

A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models

Toward Uncertainty Quantification for Supervised Classification

From Data to Decisions: Placing Machine Learning Challenges In Context

Establishing Uniform Image Segmentation Ground Truth Protocols for Uncertainty Quantification and Improved Model Evaluation

Data-Driven Uncertainty Quantification for Multi-Sensor Analytics

Establishing Uniform Image Segmentation Ground Truth Protocols for Uncertainty Quantification and Improved Model Evaluation

Increasing Trust By Quantifying Uncertainty

From Data to Decisions: Placing Machine Learning Challenges in Context

Uncertainty Quantification for Machine Learning