Marc Brittain
Chief Scientist at Johns Hopkins Applied Physics Laboratory
Washington DC-Baltimore Area United States
Summary
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Senior🎓
Top SchoolMarc Brittain is Chief Scientist at Johns Hopkins Applied Physics Laboratory and a PhD-trained researcher specializing in deep and multi-agent reinforcement learning for air traffic management and advanced air mobility. Over nine years he has advanced RL methods to ensure safe separation in high-density, heterogeneous airspace and pushed state-of-the-art performance on benchmarks like Atari and StarCraft II. His work spans computer vision, NLP, and large-scale distributed RL on cloud and HPC clusters, focusing on how to scale RL beyond current limits. Prior roles at MIT Lincoln Laboratory and leadership in AIAA’s Air Transportation Systems committee reflect a blend of operationally driven research and community engagement. He combines rigorous aerospace engineering training with practical systems experience, often translating lab algorithms into real-world airspace safety solutions. Colleagues find him approachable and eager to discuss AI, with a consistent commitment to responding to outreach.
9 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.) In Physics with a Minor in Mathematics., Physics, Bachelor of Science (B.S.) In Physics with a Minor in Mathematics., Physics at University of North Carolina WilmingtonDoctor of Philosophy - PhD, Aerospace, Aeronautical and Astronautical Engineering, Doctor of Philosophy - PhD, Aerospace, Aeronautical and Astronautical Engineering at Iowa State University
Github Skills (23)
wgs8410
simulation9
utm9
aircraft9
conflict-resolution9
traffic9
sequence8
flight-simulation8
aviation8
simulator8
replay7
python7
drones6
geometry4
geospatial4
Programming languages (1)
Github contributions (5)
marcbrittain/Reinforcement_Learning
Nov 2017 - Sep 2018
Contributions:8 commits, 5 pushes, 1 branch in 10 months
marcbrittain/PyGeoShape
Dec 2021 - Jan 2022
3D extension built off of shapely to make working with geospatial/trajectory data easier in python.
Contributions:3 releases, 32 commits, 6 PRs in 20 days
shapelygeospatialpythongeographytrajectory
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