

Daniel Rabosky
Professor Daniel Rabosky is a distinguished scholar with a robust background in ecology and evolutionary biology, currently serving as a professor and associate curator at the University of Michigan. His research is centered on unraveling the intricate patterns of species diversification and phenotypic evolution. By developing theoretical frameworks and computational tools, Professor Rabosky aims to deepen our understanding of evolutionary processes using evolutionary trees, the fossil record, and phenotypic data. His methodological expertise spans a range of advanced techniques, including supervised and unsupervised learning, Markov chain Monte Carlo, and latent feature models. These approaches enable him to explore complex evolutionary questions and contribute significantly to the field of macroevolution. Professor Rabosky's work is particularly focused on speciation and biodiversity theory, areas in which he has made substantial contributions. A significant aspect of his research involves the evolution and ecology of Australian reptiles, where he combines fieldwork, molecular phylogenetics, and mathematical modeling to gain insights into these unique ecosystems. His interdisciplinary approach allows him to address questions about how species evolve and adapt over time, contributing to a broader understanding of biodiversity. Professor Rabosky's research is characterized by its integration of empirical data with theoretical models, providing a comprehensive view of evolutionary dynamics. His work not only advances scientific knowledge but also offers practical tools and frameworks for other researchers in the field. In addition to his research, Professor Rabosky is committed to education and mentorship, guiding the next generation of scientists in the principles and practices of evolutionary biology. His dedication to both research and teaching makes him a valuable asset to the academic community at the University of Michigan and beyond.
Research Interests
Publications
, 1207-1217, 2020-03-31
, 776-790, 2022-02-20
, 2018-09-06
, 1313-1325, 2014-04-17
, 336-362, 2024-04-15