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.
Global simulations of magnetospheres (GPU acceleration)
Global planetary magnetosphere simulations beyond ideal MHD, accelerated by graphics processing units.
Physics-Informed Neural Networks
Center for HelioAnalytics
The Center for HelioAnalytics (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.
Global Magnetosphere Simulation Code
M4OPT
Python open-source toolkit for scheduling telescope observations using Mixed-Integer Linear Programming; main goal is for follow-up observations of gamma-ray events/black hole mergers.
- Robert H. Goddard Award 2022
- HSD Peer Award 2018
- NPP Fellowship 2016
- Stebbins Award (UW-Madison Astronomy) 2015
- NASA GSRP 2012
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.
