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Publication	Type	Year
Generating Uncertainty Distributions for Seismic Signal Onset Times Bulletin of the Seismological Society of America Matthew Gregor Peterson, David John Stracuzzi, Christopher J. Young, Charles Vollmer, Ronald Brogan https://www.osti.gov/search/identifier:1781564	Journal Article – 2021 Journal Article	2021
Integrating Physics through Machine Learning Loss Conference on Data Analysis Charles Vollmer, James Derek Tucker, Matthew Thomas Martinez, Edward Steven Jimenez, Reese Wayne Davies, Gabriella Dalton https://www.osti.gov/search/identifier:1767375	Conference Paper – 2020 Conference Paper	2020
Event Detection and Characterization in a Network of Subsurface Sensors CoDA 202, Conference on Data Analysis Charles Vollmer, David John Stracuzzi, Paul Conrad Schwering, Christian Poppeliers, Didem G. Beskardes	Abstract – 2020 Abstract	2020
Integrating Physics Knowledge in Multi-Sensor Machine Learning Models CoDA 2020, Conference on Data Analytics Charles Vollmer, David M. Martinez, James Derek Tucker, Edward Steven Jimenez, Reece Davies, Gabriella Dalton	Abstract – 2020 Abstract	2020
Improving Object Classifier Performance Using a Pattern of Life Conference on Data Analysis Stephen T. Jones, David John Stracuzzi, Charles Vollmer https://www.osti.gov/search/identifier:1766666	Conference Paper – 2020 Conference Paper	2020
Incorporating Scientifically Informed Constraints into Machine Learning Models for Material Classification from Hyperspectral-CT Scans SIAM Conference on Imaging Science Matthew Thomas Martinez, James Derek Tucker, Edward Steven Jimenez, Charles Vollmer, Richard McClanahan, Reese Wayne Davies, Gabriella Dalton	Abstract – 2020 Abstract	2020
Real-time Subsurface Event Assessment and Detection (RESEAD) Machine Learning R&D Workshop David John Stracuzzi, Charles Vollmer, Christian Poppeliers, Gungor Didem Beskardes, Paul Conrad Schwering, Avery Ted Cashion, Bart G van Bloemen Waanders, Chester J Weiss, Edward N Matteo, Giorgia Bettin https://www.osti.gov/search/identifier:1645887	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Event Detection and Characterization in a Network of Subsurface Sensors American Geophysical Union (AGU) Fall Meeting 2019 Charles Vollmer, Alex Fout, Giorgia Bettin, Edward N Matteo, Paul Conrad Schwering, Christian Poppeliers, Gungor Didem Beskardes, Joseph Lee Hart, Bart G van Bloemen Waanders, Chester J Weiss, David John Stracuzzi, Avery Ted Cashion	Abstract – 2019 Abstract	2019
RESEAD Analytics Total Oil and Gas Visit Charles Vollmer, David John Stracuzzi, Gungor Didem Beskardes, Christian Poppeliers, Paul Conrad Schwering, Edward N Matteo https://www.osti.gov/search/identifier:1645648	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Robust Arrival Time Uncertainty Estimation Using Gaussian Blurring 2019 Seismological Society of America Annual Meeting Matthew Gregor Peterson, Charles Vollmer, David John Stracuzzi, Christopher J. Young	Abstract – 2019 Abstract	2019
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
Bayesian Seismic Event Locator Using Circular Back Azimuth Data Conference on Data Analysis Charles Vollmer, Daniel Hernandez-Stumpfhauser https://www.osti.gov/search/identifier:1497571	Conference Paper – 2018 Conference Paper	2018
Bayesian Seismic Event Locator Using Circular Back Azimuth Data Conference on Data Analysis 2018 Charles Vollmer	Abstract – 2018 Abstract	2018
Change Point Uncertainty Quantification: First Arrival Picks for Seismic Signals Nips WorkshopTime Series Analysis Matthew Gregor Peterson, David John Stracuzzi, Charles Vollmer https://www.osti.gov/search/identifier:1481487	Conference Paper – 2017 Conference Paper	2017
Using Data-Driven Uncertainty Quantification to Support Decision Making Cis Rf Erb David John Stracuzzi, Matthew Gregor Peterson, Charles Vollmer https://www.osti.gov/search/identifier:1465020	Display or Poster (non-conference) – 2017 Display or Poster (non-conference)	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 of Refined Seismic Arrival Times 2017 Annual Meeting of the Seismological Society of America Charles Vollmer, Matthew Gregor Peterson, David John Stracuzzi https://www.osti.gov/search/identifier:1456530	Conference Paper – 2017 Conference Paper	2017
Uncertainty Analysis of an Information Theoretic Estimation of Seismic Signals New Mexico Student Conference, CS Department Matthew Gregor Peterson, Charles Vollmer, David John Stracuzzi https://www.osti.gov/search/identifier:1456414	Conference Paper – 2017 Conference Paper	2017
Uncertainty Estimation of Onset Arrival Times Using Parametric Bootstrap of Auto-Regressive Time Series Seismological Society of America Spring Meeting, 2017 Charles Vollmer, David John Stracuzzi, Matthew Gregor Peterson	Abstract – 2017 Abstract	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
Document Title	Type	Year

Publications

Generating Uncertainty Distributions for Seismic Signal Onset Times

Integrating Physics through Machine Learning Loss

Event Detection and Characterization in a Network of Subsurface Sensors

Integrating Physics Knowledge in Multi-Sensor Machine Learning Models

Improving Object Classifier Performance Using a Pattern of Life

Incorporating Scientifically Informed Constraints into Machine Learning Models for Material Classification from Hyperspectral-CT Scans

Real-time Subsurface Event Assessment and Detection (RESEAD)

Event Detection and Characterization in a Network of Subsurface Sensors

RESEAD Analytics

Robust Arrival Time Uncertainty Estimation Using Gaussian Blurring

Using Uncertainty to Understand Machine Learning Models and Decisions

Bayesian Seismic Event Locator Using Circular Back Azimuth Data

Bayesian Seismic Event Locator Using Circular Back Azimuth Data

Change Point Uncertainty Quantification: First Arrival Picks for Seismic Signals

Using Data-Driven Uncertainty Quantification to Support Decision Making

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 of Refined Seismic Arrival Times

Uncertainty Analysis of an Information Theoretic Estimation of Seismic Signals

Uncertainty Estimation of Onset Arrival Times Using Parametric Bootstrap of Auto-Regressive Time Series

Uncertainty in Data Analytics