Skip Ribbon Commands
Skip to main content

Researcher

Profile Picture

James Money, Ph.D.

Applied Visualization Laboratory Lead

Research Areas:
Applied Visualization; Data Science; Numerical Differential Equations; Numerical Linear Algebra

Biography:
Dr. James H. Money is Applied Visualization Laboratory Lead for Idaho National Laboratory. He has more than 20 years of experience in a variety of fields including immersive visualization in both academic and industry settings. His experience includes leading the first Department of Defense initiative to provide live data sets inside a Cave Automatic Virtual Environment (CAVE), touch displays, and large format video walls from 2007 to 2011. Before coming to INL, he led multiple efforts in the modeling & simulation community to bridge 30-year- old solutions to leading edge products, both in the areas of computations and visualization. He also has worked extensively in geospatial technologies over his career, including seven years working at National Geospatial-Intelligence Agency (NGA) as well as several other intelligence agencies. Dr. Money earned his doctorate and master’s degrees in mathematics from the University of Kentucky, and a bachelor’s degree in computer science from James Madison University. His prior academic work includes variational methods for image processing, numerical differential equations and linear algebra, and cluster computing. His industrial work includes large-scale data analysis of intelligence for the government and leading clients through transformational change that has resulted in an order of magnitude of costs savings in their projects.

Education:

​Ph.D., Mathematics - University of Kentucky

M.S., Mathematics - University of Kentucky

B.S., Computer Science - James Madison University

Publications:

GPGPU Enabled Ray Directed Adaptive Volume Visualization for High Density Scans. James H. Money, Marko J. Sterbentz, Nathan V. Morrical, Thomas Szewczyk, and Landon Woolley, In Proceedings of Practice & Experience in Advanced Research Computing 2018, 2018.


IQ-Stations: Advances in State-of-the-Art Low Cost Immersive Displays for Research and Development, William Sherman, James H. Money, Eric Whiting, and Shane Grover, In Proceedings of Practice & Experience in Advanced Research Computing 2018, 2018.


Evaluation of Scientific Workflow Effectiveness for a Distributed Multi-User Multi-Platform Support System for Collaborative Visualization, Rajiv Khadka, James H. Money, and Amy Banic, In Proceedings of Practice & Experience in Advanced Research Computing 2018, 2018.


Support Collaboration Across Geographically Distributed Users Using Heterogeneous Virtual Reality Systems. Khadka, Rajiv, James Money, and Amy Banic.  International Conference on Human-Computer Interaction. Springer, 2018.

 

​Money, James H., and Thomas Szewczyk. "Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis." Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact. ACM, 2017.

 

Towards Cross-Surface Immersion Using Low Cost Multi-Sensory Output Cues to Support Proxemics and Kinesics Across Heterogeneous Systems, Amy Banic, Rajiv Khadka, and James H. Money, Cross-Surface, 2016.

 

Enhancements to VTK enabling Scientific Visualization in Immersive Environments, Patrick O'Leary, Sankhesh Jhaveri, Aashish Chaudhary, William Sherman, Ken Martin, David Lonie, Eric Whiting and James Money, IEEE Virtual Reality, 2017.

 

Total Variation Minimizing Blind Deconvolution With Shock Filter Reference, J. Money and S. H. Kang, Image and Vision Computing, 2008.

 

Implementing the Discretized Picard’s Method, J. Money, AMS Contributed Papers, 2007.

 

Total Variation Based Semi-Blind Image Deconvolution, J. Money, AMS Contributed Papers, 2006.

 

EIGIFP: A MATLAB Program for Solving Large Symmetric Generalized Eigenvalue Problems, J. Money and Q. Ye, ACM Transactions on Mathematical Software. 2005.

 

A General ODE & PDE Solver Using Picard’s Method, Maryland-DC-Virginia MAA Section Meeting, Contributed Session, 1998.

Presentations:

GPGPU Enabled Ray Directed Adaptive Volume Visualization for High Density Scans, Practice & Experience in Advanced Research Computing, July 2018.


IQ-Stations: Advances in State-of-the-Art Low Cost Immersive Displays for Research and Development, Practice & Experience in Advanced Research Computing, July 2018.


Enabling Large Scale Visualization: GPGPU Enabled Adaptive Volume Visualization, Center for Advanced Energy Studies Seminar, June 2018.


INL Applied Visualization DOE Site Update, DOE Computer Graphics Forum, May 2018.

 

​Idaho National Laboratory Applied Visualization Site Update, DOE Computer Graphics Forum, May 2017.


Integrating Commercial & Government Software for Immersive In-situ Visualization, DOE Computer Graphics Forum, May 2017.


Live Integrated Visualization Environment, Practice and Experience in Advanced Research Computing, July 2017.


Scientific & Intelligence Exascale Visualization Analysis System: Theory & Foundations, SIGGRAPH, August 2017.


Transitioning to Rapid Development for Immersive Environments, THE CAAV, October 2017.

 

Using Virtual Reality for Scientific Research, Idaho National Laboratory, September 2016.

 

Applied Visualization: Using Immersive Environments for Real World Applications, Idaho National Laboratory, February 2016.

 

Improving Simulation Visualization through Innovative Interfaces and GIS Technology, Alion Science & Technology, January 2015.

 

Implementing Web based Simulations in a Data Constricted Environment, Alion Science & Technology, Architecture Review Board, July 2013.

 

Using the SVD to Secure Networks, Booz Allen Hamilton, June 2012.

 

Applied Visualization: Mathematic approaches of large scale real-time data analysis, Joint Forces Command, January 2011.

 

Applying Discretized Picard’s Method to non-linear PDEs, James Madison University, Invited Speaker, April 2010.

 

Implementing the Discretized Picard’s Method, AMS Contributed Session, National Joint Meetings, January 2007.

 

Variational Methods for Image Deblurring and Discretized Picard’s Method, University of Kentucky, Graduate Colloquium, April 2006.

 

Co-Chair, AMS Session on Numerical Analysis and Fluid Mechanics, National Joint Meetings, AMS Contributed Session, January 2006.

 

Total Variation Based Semi-Blind Image Deconvolution, National Joint Mathematics Meeting, AMS Contributed Session, January 2006.

 

Total Variation Image Deblurring, University of Kentucky, Graduate Colloquium, April 2005.

 

Total Variation Image Deblurring, James Madison University, Departmental Colloquium, March 2005.

 

Using Picard Iteration and Cauchy Products to Solve Initial Value Problem Ordinary Differential Equations, Maple Summer Workshop, Waterloo Canada, Contributed Session, July 2004.

 

Block Inverse Free Algorithms for Computing Large Sparse Eigenvalue Problems, James Madison University, Departmental Colloquium, March 2004.

 

Using Picard Iteration to Solve ODEs and PDEs, University of Kentucky, Mathematics Graduate Student Colloquium, March 2003.

 

Advanced MPI Tutorial, University of Kentucky, Mathematics Numerical Analysis Seminar, November 2002.

 

Introduction to Message Passing Interface(MPI), University of Kentucky, Mathematics Numerical Analysis Seminar, October 2002.

 

Introduction to Parallel Computations and Beowulf Clusters, University of Kentucky, Mathematics Numerical Analysis Seminar, September 2002.

 

A General ODE & PDE Solver Using Picard’s Method, Maryland-DC-Virginia MAA Section Meeting, Contributed Session, March 1998.

Version: 5.0
Created at 10/12/2016 10:25 AM by Phyllis L. King
Last modified at 11/29/2018 12:11 PM by hailey.goddard@inl.gov