Findley Finseth

Findley Finseth serves as Assistant Professor of Genomics in the Natural Sciences Department at the Claremont Colleges, where her research program investigates evolutionary drivers of biodiversity through integrated genomic and classical genetic approaches. Her work focuses on California native wildflowers (Mimulus), examining genetic variation maintenance, adaptation mechanisms, and speciation processes.

Education:

• B.S. in Biology, University of Virginia
• Ph.D. in Ecology and Evolution, Cornell University (Presidential Life Sciences Fellow)
• Post-doctoral research, University of Montana

Her research spans selfish genetic element evolution, thermal adaptation in extreme environments like Yellowstone National Park, and genomic architecture of reproductive traits. By combining field studies with molecular techniques, her lab addresses fundamental questions about how genomes evolve under natural selection and intragenomic conflict.

Publications from 2013-2015 reveal consistent focus on evolutionary mechanisms across plant and animal systems, with emphasis on gene duplication, reproductive gland evolution, and next-generation sequencing methodology. These works demonstrate her interdisciplinary approach bridging genomics, ecology, and classical genetics to solve complex evolutionary puzzles.

Awards:

• Presidential Life Sciences Fellow, Cornell University
• P.E.O. Scholar
• Multiple teaching awards during doctoral studies

Professor Finseth actively mentors undergraduate researchers, as evidenced by co-authorships on publications. Her research program is supported by competitive grants from the National Science Foundation, American Museum of Natural History, Cornell Lab of Ornithology, and Andrew W. Mellon Foundation, enabling fieldwork, genomic analyses, and student training.

Her lab conducts cutting-edge evolutionary genomics research on Mimulus wildflowers, with projects ranging from selfish gene dynamics to thermal adaptation in natural populations, providing students with hands-on experience in both computational and experimental approaches to evolutionary biology.