Brian Nord is an astrophysicist and machine learning researcher at Fermi National Accelerator Laboratory (Fermilab). He uses computational techniques to identify and remove gravitational lensing from maps of the cosmic microwave background, to classify merging galaxies, and much more.

See Brian’s website for a comprehensive list of his many research interests and publications.

Brian Nord earned his undergraduate degree in physics from Johns Hopkins University in 2003, then went on to earn a PhD from the University of Michigan in 2010.

Brian now works as a research scientist at Fermi National Accelerator Laboratory (Fermilab). He also leads several outreach initiatives, including Citizen Science Scientist Engagement for the Rubin Observatory and leads the Space Explorers program at the Kavli Institute for Cosmological Physics.

In the modern era of astronomy, the data from telescopes and the simulations needed to understand astrophysical systems are becoming increasingly expensive to process with traditional techniques. Brian Nord leverages machine learning techniques to make it possible to remove noise from complicated datasets, like lensing of the CMB and to simulate the formation of structure in the universe.

Understanding how our universe formed and evolves lies at the heart of all physics and the tools developed by Brian Nord make it possible to make the new discoveries that continue to move physics forward.

http://briandnord.com/

Wikipedia Brian Nord

Watch some of Brian’s lectures on science, outreach, and advocacy