Sept. 21, 2022, 1:11 a.m. | Giovanni Charles, Timothy M. Wolock, Peter Winskill, Azra Ghani, Samir Bhatt, Seth Flaxman

stat.ML updates on arXiv.org arxiv.org

Epidemic models are powerful tools in understanding infectious disease.
However, as they increase in size and complexity, they can quickly become
computationally intractable. Recent progress in modelling methodology has shown
that surrogate models can be used to emulate complex epidemic models with a
high-dimensional parameter space. We show that deep sequence-to-sequence
(seq2seq) models can serve as accurate surrogates for complex epidemic models
with sequence based model parameters, effectively replicating seasonal and
long-term transmission dynamics. Once trained, our surrogate can predict …

arxiv bayesian bayesian inference epidemic inference seq2seq

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Data Science Analyst- ML/DL/LLM

@ Mayo Clinic | Jacksonville, FL, United States

Machine Learning Research Scientist, Robustness and Uncertainty

@ Nuro, Inc. | Mountain View, California (HQ)