Brian Gallagher is a computer scientist in LLNL’s Center for Applied Scientific Computing, where he leads the Data Science & Analytics Group. His primary research interest is developing and applying machine learning methods for scientific applications. Brian has served as a machine learning lead on numerous proposals and funded projects in the areas of nuclear threat reduction, materials discovery and optimization, and network science. His work contributes to multiple Global Security, Weapons and Complex Integration, and Physical and Life Sciences programs at LLNL. Brian serves on proposal review committees, has served as Scientific Editor of the Computing Annual Report, and has provided mentorship to dozens of LLNL staff.

Brian’s research also impacts the broader data science, network science, and machine learning communities. In 2017, Brian received the IEEE Infocom Test of Time Award for his seminal work on routing messages in disruption-tolerant networks. He has served as a reviewer for dozens of journals and conferences and currently serves on the ACM SIGKDD Conference Senior Program Committee. Brian has also led more than a dozen academic collaborations, mentored over 70 graduate and undergraduate students, and currently serves as the Director of the LLNL/UC Data Science Challenge, an educational program that provides University of California students the opportunity to collaborate on real-world research problems with LLNL staff.

For up-to-the-minute information on Brian’s publications, see his Google Scholar Profile.

• LLNL Service Award, 2020
• LLNL Physical & Life Sciences Directorate Award for Excellence in Publication, 2020
• LLNL Design Physics Division Publication Award, 2020
• LLNL Center for Applied Scientific Computing Award for Exceptional Customer Service, 2019
• IEEE International Conference on Computer Communications Test of Time Publication Award, 2017
• IEEE Data Science and Advanced Analytics Best Research Paper Honorable Mention, 2016
• LLNL Service Award, 2015
• IEEE International Conference on Data Mining Best Paper Award Runner Up, 2009
• ACM KDD Cup Winner, 2003