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- PhD in Applied Mathematics, Politecnico di Torino (2017)
- M.Sc. in Mathematics, Universita' di Torino (2013)
- B.Sc. in Mathematics, Universita' di Torino (2010)
Department of Mathematics and Statistics
print/web
print/web
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Area(s) of expertise
Topological data analysis, computational topology, network neuroscience, network science, math.
BIO
Dr. Alice Patania is an Assistant Professor in the Department of Mathematics and Statistics at the University of Vermont and core faculty of the Vermont Complex Systems Institute. As a computational topologist, her research develops rigorous mathematical frameworks that bridge pure mathematics with practical applications in neuroscience and complex systems. Her work addresses how to model and analyze higher-order interactions in networks—relationships that involve multiple entities simultaneously rather than simple pairwise connections.
Her mathematical innovations extend to social networks, where she is developing sheaf-based models for belief dynamics that capture hierarchical structure and coherence properties in complex social systems. Her translational research program combines rigorous topological theory with robust computational tools, establishing new paradigms for analyzing complex biological and social phenomena where traditional mean-field approaches fail to capture emergent collective behaviors.
Publications
Bio
Dr. Alice Patania is an Assistant Professor in the Department of Mathematics and Statistics at the University of Vermont and core faculty of the Vermont Complex Systems Institute. As a computational topologist, her research develops rigorous mathematical frameworks that bridge pure mathematics with practical applications in neuroscience and complex systems. Her work addresses how to model and analyze higher-order interactions in networks—relationships that involve multiple entities simultaneously rather than simple pairwise connections.
Her mathematical innovations extend to social networks, where she is developing sheaf-based models for belief dynamics that capture hierarchical structure and coherence properties in complex social systems. Her translational research program combines rigorous topological theory with robust computational tools, establishing new paradigms for analyzing complex biological and social phenomena where traditional mean-field approaches fail to capture emergent collective behaviors.