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Eva L. Dyer is an Assistant Professor at the University of Pennsylvania, with research interests primarily in Computational Neuroscience, focusing on Machine Learning and Signal Processing. Her work emphasizes the development of self-supervised learning methodologies applicable to complex neural data. She has contributed significantly to understanding neural representation learning and brain-machine interfaces, with an aim to inform the design of intelligent systems that can better interface with human and animal brains. Dyer's innovative approaches and contributions are evident through her publications, which highlight her ongoing work in hierarchical transport and optimal data alignment in multimodal contexts. She aims to create scalable frameworks that will enhance our understanding and capabilities in neuroscience. Her collaborative work spans across various interdisciplinary projects and consistently seeks to bridge theoretical frameworks with practical applications in technology and neuroscience. Dyer obtained her education from reputable institutions and has been involved in a myriad of impactful research initiatives that have advanced the fields of biomedical engineering and computational modeling.
University of Pennsylvania • Philadelphia, PA
Teaching and conducting research in the fields of computational neuroscience and machine learning.
Wharton Doctoral programs cover fields like Finance, Marketing, Management, and Operations, Information and Decisions.