My mathematical background lies at the intersection of algebraic and low dimensional topology. My current work is highly interdisciplinary, collaborating with neuroscientists, mathematicians, and high-performance computing experts to apply topological data analysis to neuroscience, building tools to better understand brain function.
I study data coming from simulations of brain models, from the most simple and conceptually driven models to the most complex data driven models. In either case the main task is the same: to develop principled ways to extract patterns or features that provide insight into the structure of the system and its inner working.
While my work has primarily focused on mathematical approaches in neuroscience, at the MPI-CBG I am now aiming to apply these methods more broadly to biological systems, exploring how topology and network structure can reveal general organizational and functional principles across living systems.
For more information please visit my personal homepage at https://danielaegassan.github.io/
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