Grant Adams
Research Mathematical Statistician
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolGrant Adams is a Research Mathematical Statistician with nine years of experience applying machine learning, Bayesian and likelihood-based hierarchical modeling, and decision analysis to natural resource and fisheries management across academia, industry, and government. Based in Seattle and currently at NOAA, he has built and productionized complex R and C++ tools for spatio-temporal regression, age-structured population models, and probabilistic decision frameworks that directly inform federal regulations and multimillion-dollar management decisions. His work blends advanced computational tools (STAN, TMB, JAGS, Git, Python) with rigorous simulation testing to reduce bias in assessments by up to 39% and has supported high-profile conservation and fishery outcomes. A collaborative communicator, he has led cross-sector teams, authored numerous scientific papers and government reports, and brings practical field experience—from Peace Corps environmental programs in the Andes to international research on shark and whale populations—paired with advanced Spanish fluency.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of WashingtonMaster of Science - MS, Master of Science - MS at The University of Southern MississippiBachelor of Science - BS, Bachelor of Science - BS at Denison UniversityUniversity of Otago
Spanish
Github Skills (20)
tmb10
linear-models10
r-language10
glmmtmb10
estimate8
computation7
resampling7
python3
statistics3
r2
science2
data-visualization2
shiny1
simulation1
marine1
Programming languages (4)
Github contributions (5)
grantdadams/Rceattle
Dec 2017 - Oct 2022
TMB based age-structured multi-species model. Estimation, simulation, management strategy evaluation.
Contributions:713 commits, 5 PRs, 394 pushes in 4 years 10 months
tmb
antarctic-humpback-2019-assessment/HumpbackSIR
Aug 2019 - Nov 2023
Use Sample-Importance-Resampling to estimate Bayesian surplus production population dynamics models using the "backward" approach
Contributions:4 PRs, 25 pushes, 5 branches in 4 years 3 months
resamplingabundanceestimateimportance
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