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Publication	Type	Year
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
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
Using Data-Driven Uncertainty Quantification to Support Decision Making Statistical Data Science Conference Charles Vollmer, Matthew Gregor Peterson, David John Stracuzzi, Maximillian Gene Chen https://www.osti.gov/search/identifier:1513614	Conference Paper – 2017 Conference Paper	2017
Using Data-Driven Uncertainty Quantification to Support Decision Making Statistical Data Science Conference Matthew Gregor Peterson, Charles Vollmer, David John Stracuzzi, Maximillian Gene Chen https://www.osti.gov/search/identifier:1459094	Conference Paper – 2017 Conference Paper	2017
Data-Driven Uncertainty Quantification for Remote Sensing Customer Meeting on Uncertainty David John Stracuzzi, Maximillian Gene Chen, Michael Christopher Darling, Stephen Michael Dauphin, Matthew Gregor Peterson, Charles Vollmer https://www.osti.gov/search/identifier:1457960	Presentation (non-conference) – 2017 Presentation (non-conference)	2017
Uncertainty in Data Analytics UNM invited talk David John Stracuzzi, Maximillian Gene Chen, Michael Christopher Darling, Stephen Michael Dauphin, Matthew Gregor Peterson, Charles Vollmer, Christopher J. Young https://www.osti.gov/search/identifier:1413588	Presentation (non-conference) – 2016 Presentation (non-conference)	2016
Multimodal Data Integration Under Uncertainty Conference on Data Analytics David John Stracuzzi, David G. Robinson, Matthew Gregor Peterson, Stephen Michael Dauphin, Maximillian Gene Chen, Robert Riley https://www.osti.gov/search/identifier:1422161	Conference Paper – 2016 Conference Paper	2016
Multimodal Data Integration Under Uncertainty Conference on Data Analytics David John Stracuzzi, David G. Robinson, Matthew Gregor Peterson, Stephen Michael Dauphin, Maximillian Gene Chen, Robert Riley	Abstract – 2016 Abstract	2016
Document Title	Type	Year

Results 1–25 of 26

Publications

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

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

Using Data-Driven Uncertainty Quantification to Support Decision Making

Using Data-Driven Uncertainty Quantification to Support Decision Making

Data-Driven Uncertainty Quantification for Remote Sensing

Uncertainty in Data Analytics

Multimodal Data Integration Under Uncertainty

Multimodal Data Integration Under Uncertainty