Summary
Daniel Reuman is a quantitative ecologist and senior scientist who translates mathematical and statistical theory into practical tools for understanding ecology and global change. With a PhD in Mathematics and over a decade leading funded research groups at Imperial College and the University of Kansas, he builds and ships open-source software (notably R packages wsyn and tsvr) that embed novel methods into reproducible workflows. His work spans machine learning, modeling, and algorithm development and has appeared in high-impact journals such as PNAS, Science, and Nature Climate Change. A recipient of the Humboldt Research Award and McDonnell Complex Systems Scholar award, he combines rigorous theory with effective science communication to diverse audiences. He is actively exploring how ecological quantitative methods can be deployed in business and policy contexts, bringing academic depth to applied decision-making.
9 years of coding experience
7 years of employment as a software developer
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at Harvard University
Doctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at University of Chicago