Edward Ionides
Professor at University of Michigan
Ann Arbor, Michigan, United States
Summary
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Senior🎓
Top SchoolEdward Ionides is a Professor of Statistics at the University of Michigan with over a decade of experience developing computational inference methods for nonlinear, partially observed stochastic dynamic systems. He originated iterated filtering, a simulation-based likelihood approach that unlocked statistical analysis of complex epidemiological and ecological time series, and has applied it to diseases such as malaria, cholera, measles and HIV. His work bridges theory and practice, spawning a new class of stochastic compartmental models with noisy transition rates and extending Monte Carlo techniques to genetic and spatio-temporal data. Trained at Cambridge (Math) and UC Berkeley (Ph.D. Statistics), he continues to refine iterated filtering algorithms and translate methodological advances into tools for real-world public health and ecological problems.
13 years of coding experience
Doctor of Philosophy (Ph.D.), Statistics, Doctor of Philosophy (Ph.D.), Statistics at University of California, BerkeleyBachelor's Degree, Mathematics, Bachelor's Degree, Mathematics at University of Cambridge
Stackoverflow
1reputation
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Github Skills (33)
prediction-model10
r-package10
abc10
resampling10
statistical-inference10
pls10
linear-models10
stochastic-processes10
state-space10
gmm10
markov-model10
particle-filter10
markov10
hidden-markov-model10
arima9
Programming languages (4)
Github contributions (5)
ionides/401f18
Jul 2018 - Dec 2018
Contributions:194 commits, 294 pushes, 2 branches in 4 months
ionides/531w21
Oct 2020 - Nov 2021
Contributions:92 commits, 81 pushes, 1 branch in 1 year
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