Ryan McGranaghan is a Data Scientist and Research Scientist at the NASA Jet Propulsion Laboratory, where he works with the Machine Learning and Instrument Autonomy (MLIA) group to apply data science techniques robustly and responsibly to the Earth and Space Sciences, to cultivate cross-NASA Center collaborations, and to explore more cohesive and plural scientific communities. He is also a core team member for the NASA Transformation to Open Science (TOPS) initiative, improving the accessibility, inclusivity, and reproducibility of science. His career as in his life is about creating and cultivating transdisciplinary and trans-community connections for the sake of scientific discovery and flourishing.

Prior to joining JPL, Ryan spent four years as the Principal Data Scientist and Aerospace Engineering Scientist at Orion Space Solutions in Boulder, CO, where he led data science and machine learning efforts to improve our understanding of the Earth’s space environment and began a DC branch of the company. Ryan began this role after completing a Jack Eddy Living With a Star Postdoctoral Fellowship at NASA JPL, during which he studied the Earth’s and solar system planets’ interactions with the Sun. He also works extensively across NASA Centers, including Goddard Space Flight Center and the Jet Propulsion Laboratory.

In 2013, Ryan received the National Science Foundation Graduate Research Fellowship, in recognition of profound vision for the future of Earth and Space Science research. He was also awarded the illustrious NASA Jack Eddy Fellowship designed to train the next generation of space physics researchers. Most recently, he was selected by the prestigious NASA Heliophysics Early Career Investigator Program, which provides support for the most promising young scientists conducting Heliophysics research.

In all of his work, Ryan takes a multi-disciplinary (what he terms ‘antidisciplinary’) approach to the study of space, bringing together traditional space physics with innovation from the fields of data science and sociology.

He is also the creator, producer, and host of The Origins Podcast, the founder and facilitator of The Flourishing Salons, gatherings that embrace liminality and plurality of thought and give rise to new connections, communities, and capabilities for flourishing systems.

Ryan’s transdisciplinary passions have led to involvement across many remarkable groups, including: the JPL Data Science Working Group, the NASA Frontier Development Lab artificial intelligence R&D incubator, the Santa Fe Institute, and the Cultural Programs of the National Academy of sciences. Prior to joining JPL, Ryan received the Visiting Young Scientist Fellowship to join the Dartmouth College School of Engineering faculty. During his six-month visiting tenure he created and taught a graduate-level course on statistical inference and data assimilation and conducted research across the engineering, applied math, and physics departments. Ryan was selected as a National Science Foundation Fellow to complete his Ph.D. research at the University of Colorado Boulder, and completed his degree in Aerospace Engineering Sciences in the Fall of 2016. He also holds a Master’s Degree in Aerospace Engineering Sciences from CU Boulder and a Bachelor’s Degree in Aerospace Engineering from the University of Tennessee.

Ryan has enjoyed research experiences with Los Alamos National Laboratories, the National Center for Atmospheric Research High Altitude Observatory, and the NASA Marshall Space Flight Center, to name a few.

Ryan is a passionate communicator and entrepreneur of science. He was selected to give a TED talk in April 2015 on the topic of space weather, and continually strives to move audiences and more effectively communicate science through compelling storytelling and data visualization.

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Education

• Aerospace Engineering Sciences (PhD) - University of Colorado at Boulder
• Aerospace Engineering (BS) - University of Tennessee

Research Interests

• Heliophysics & Space Weather
• Data-Driven Science
• Network Science
• Knowledge Graphs
• Systems/Complexity Science
• Machine Learning
• Convergence Research