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Daniel Persson is a professor at the University of Gothenburg, specializing in geometric deep learning, equivariance, and automorphic forms. His research interests span various advanced topics in mathematical physics and artificial intelligence, including the application of vision transformers in neural networks and the exploration of Fourier coefficients in significant mathematical representations. Persson has contributed to leading journals such as the Artificial Intelligence Review and the Journal of High Energy Physics, addressing complex subjects including equivariant neural networks and reduction principles in automorphic forms. His work aims to further understand the underlying mathematical frameworks that drive modern machine learning and theoretical physics.
University of Gothenburg • Gothenburg, Sweden
Teaching and research in advanced mathematics and its applications to artificial intelligence.
Administered by the Department of Political Science; focus on International Administration and Global Governance (IAGG).