Conference Description
As genomic information becomes increasingly accessible across model and non-model organisms, evolutionary and ecological genomics have become essential frameworks to understand how mutation processes, selection and drift shape biodiversity and phenotypes. The 2021 Gordon Research Conference on Ecological and Evolutionary Genomics will highlight recent advances in our understanding of interactions between genomes and their environment, covering a variety of scales over space and time from population studies to comparative genomics, deep evolution, and theoretical models. The conference will emphasize how new technologies and models can be leveraged to illuminate evolutionary processes across animals, plants and microbes, and ambitions to spark bold new ideas in the field of genome evolution and ecology.
Co-chairs, Sarah Kocher (Princeton University) and Camille Berthelot (Ecole Normale Supérieure, Paris) invite you to Bryant University, where we are assembling a diverse group of established and early career investigators to discuss their latest work across a wide variety of organisms. A subset of the submitted abstracts will be selected for short talk presentations and a limited number of travel grants will be available. Join us to participate in creative discussions in an inclusive social and scientific atmosphere, to empower the future research in the field
The conference will consist of nine sessions, on the topics listed below. The conference chair is currently developing their preliminary program, which will include the names of the invited speakers and discussion leaders for each of these sessions. Please check back regularly for updates to this information.
- Experimental Evolution
- Genomics of Selective Adaptation and Evolutionary Innovations
- Genomics of Rapid Evolutionary Response to Environmental Change
- Community and Ecosystems Genomics
- Comparative and Population Genomics
- Biodiversity in the Omics Era
- The Dark Side of the Genome
- Methodological Innovation in Machine Learning and Big Data Science for EEG
- Early-Career Investigator Presentations
