Sciences and Exploration Directorate

Chris Bard

(Research AST, Fields and Particles)

Chris Bard's Contact Card & Information.
Phone: 301.286.7368
Org Code: 673
Mail Code 673
Greenbelt, MD 20771

Brief Bio

Dr. Bard graduated from the University of Wisconsin with a PhD in astrophysics, studying magnetic massive stars. During his time in Madison, he was also a NASA Graduate Student Research Program Fellow from 2012-2014, developing a 3D magnetohydrodynamics code accelerated by graphics processing units. After graduating in 2016, Chris joined the Geospace Lab as a NASA Postdoctoral Program Fellow, continuing his work on extending the GPU code to simulate magnetospheres and starting to experiment with Physics-Informed Neural Networks. After a brief stopover at UMBC as a postdoc, he became a civil servant in 2019 and remains in this position today. He is currently the lead programmer for the MARBLE global magnetosphere code, and the Managing Director for the Center for HelioAnalytics, a AI/ML data science group for Heliophysics.

Research Interests

Global simulations of magnetospheres (GPU acceleration)

Solar System: Magnetospheres

Global planetary magnetosphere simulations beyond ideal MHD, accelerated by graphics processing units.

Physics-Informed Neural Networks and applications to Heliophysics/Plasma Physics

Heliophysics: Theory & Modeling

Current Projects

Center for HelioAnalytics


Dr. Bard is the Managing Director for the Center for HelioAnalytics (CfHA). CfHA establishes a Community of Practice to envision solutions using machine learning, knowledge capture, and data analytics to expand the discovery potential for key heliophysics research topics and missions. Our primary goal is to build sustainable connections in the Heliophysics Community for the purpose of supporting efforts to harness data science, machine learning, and AI to drive scientific discovery.

Neural Network Data Compression


Along with Daniel da Silva, Dr. Bard is co-leading a research effort to utilize Neural Networks for in-situ compression of plasma instrument data. Initial results trained on MMS FPI data returned a 30x compression ratio, about twice as good as the current standard onboard MMS (16x). Currently, DDS and CB are developing a lossless compression algorithm for plasma data and porting it for use onboard spacecraft FPGAs.



Dr. Bard is the lead programmer for MARBLE (The Magnetosphere-Aurora Reconnection Boundary Layer Explorer). MARBLE is a in-development global magnetosphere code solving the kinetic MHD equations. It will enable us to more directly study how magnetosphere reconnection (especially in the tail) produces electrons which travel along magnetic field lines to the ionosphere, contributing to the aurora.


  • Robert H. Goddard Award 2022
  • HSD Peer Award 2018
  • NPP Fellowship 2016
  • Stebbins Award (UW-Madison Astronomy) 2015
  • NASA GSRP 2012