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BE Seminar: “Toward a multi-task, multi-source foundation model to advance large-scale neural data analysis” (Eva Dyer, Georgia Tech)
January 18 at 3:30 PM - 4:30 PM
Neuroscience datasets are expanding rapidly in both size and volume. However, integrating information across diverse datasets to form a unified understanding of brain function remains challenging. In this talk, I will discuss our initiatives to combine datasets from various tasks, brain regions, and species into a unified ‘neurofoundation’ model. This foundational model promises to enhance data efficiency, brain-machine interface and neural decoder capabilities, and offer advanced, user-friendly tools to the broader neuroscience community. These efforts mark a significant step towards a more integrated methodology in neural data analysis.
Eva Dyer, Ph.D.
Associate Professor, Biomedical Engineering, Georgia Tech
Eva Dyer (she/they) is an Associate Professor in the Coulter Department of Biomedical Engineering at the Georgia Institute of Technology. Dr. Dyer’s research lies at the intersection of artificial intelligence and neuroscience, where she aims to both use AI to understand neural computation (AI for Neuro) and use insights from the nervous system to develop robust and lifelong AI systems (Neuro for AI). Eva completed all of her degrees in Electrical & Computer Engineering, obtaining her Ph.D. and M.S. from Rice University and a B.S. from the University of Miami. She is the recipient of a Sloan Fellowship in Neuroscience, NSF CAREER Award, Next Generation Leader Award from the Allen Institute, a McKnight Foundation Technological Innovations in Neuroscience Award, and a CIFAR Azrieli Global Scholar Award.