Wesley Cota is an Assistant Professor of Physics and computational epidemiologist with a decade of experience modeling epidemic and information spread on complex networks. He combines theoretical insight with hands-on high-performance computing to simulate large, heterogeneous systems and to incorporate human mobility and contact heterogeneity into disease forecasts for influenza, COVID-19 and dengue. His award-winning PhD research explored echo chambers, modular network complexity and efficient simulation methods, and he maintains and manages an HPC cluster that supported much of this work. In 2020 he created a widely used public COVID-19 dataset adopted by Johns Hopkins and Our World in Data, showing his work’s impact beyond academia. Comfortable across Fortran/C, Python data stacks, GIS and data mining, he blends deep modeling skills with practical data engineering to inform public health and media organizations.
11 years of coding experience
3 years of employment as a software developer
Guest PhD Candidate Physics, Guest PhD Candidate Physics at Universidad de Zaragoza
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at Universidade Federal de Viçosa
Physics, Physics at Lindau Nobel Laureate Meetings
COVID-19 data in Brazil: cases, deaths, and vaccination at municipal (city) level. Description of the data: https://github.com/wcota/covid19br/blob/master/DESCRIPTION.en.md and https://doi.org/10.1590/SciELOPreprints.362
Contributions:7694 commits, 13 PRs, 7343 pushes in 2 years 10 months
Contributions:3 releases, 18 commits, 18 pushes in 5 years 5 months
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