2019-on | Principal Investigator, Carnegie Staff Associate & Assistant Professor (By Courtesy) of Biology | Carnegie Institution for Science & Stanford University
2019 (5 mo) | Postdoc in Statistical Genetics | University of California Berkeley
2014-18 | PhD in Eco-Evolutionary Genomics | Max Planck Institute Dev. Biology
2013-14 | MSc in Quantitative Genomics | University of Edinburgh
2011-13 | Research Assistant | Biological Station of Doñana CSIC
2008-13 | Bsc in Biology | University Sevilla & Alicante
Evolutionary ecologist, plant biologist, and geneticist Moises (Moi) Exposito-Alonso is a Carnegie Staff Associate at the Departments of Plant Biology and Global Ecology from the Carnegie Institution for Science and Assistant Professor (by courtesy) of Biology at Stanford University. Moi received a B.S. in biology from the University of Seville, Spain and an MSc degree in quantitative and population genetics from the University of Edinburgh, Scotland. He earned his Ph.D. in 2018 supervised by Detlef Weigel through the EDGE program (evolution, development, ecology, genetics) at the Max Planck Institute of Biology in Tübingen, Germany. After a few months-long-postdoctoral fellowship in statistical genetics at the University of California Berkeley with Rasmus Nielsen, he joined the faculty at Carnegie and Stanford in 2019. Moi's work has been recognized by several awards, including the Otto Hahn Medal from the Max Planck Institute, the American Naturalist Young Scientist Award, the National Institutes of Health Director’s Early Independence Award, and was named Forbes’ 30 Under 30 in Science and Healthcare.
Moi investigates whether and how plants will evolve to keep pace with climate change by conducting large-scale ecological and genome sequencing experiments. He also develops computational methods to derive fundamental principles of evolution, such as how fast natural populations acquire new mutations and how past climates shaped continental-scale biodiversity patterns. His goal is to use these “first principles” and computational approaches to forecast evolutionary outcomes of populations under climate change to anticipate potential future biodiversity losses. Moi wants to use this knowledge and new genome engineering methods to help species adapt to climate change instead of becoming extinct.