Computational tools for predicting robust function in biological systems

ABSTRACT: 
Biological systems exhibit various emergent dynamics that support learning, regulation, growth, and motility. However, how robust dynamics arises from stochastic components remains unclear. Toward understanding this, I develop topological and geometrical tools to analyze the emergence of global patterns in different biological systems. To start, I will show how the geometric organization of neural responses as reflected in functional MRI reflects the speed and efficiency of effective learners. Moving to the molecular scale, I will next present stochastic networks that enable different phenomena from a global clock, stochastic growth and shrinkage, to synchronization. More broadly, my work indicates mechanisms for the enhancement of robust function and health in diverse biological systems. 

BIO:
Evelyn Tang is a group leader in the Department of Living Matter Physics at the Max Planck Institute for Dynamics and Self-Organization. Fascinated by the stability of robust function in living and active matter, she develops topological and geometric theories to predict the necessary conditions for emergent dynamics. Topics of interest include learning, information processing, and global molecular cycles. Previously, Tang was an Africk Postdoctoral Fellow at the University of Pennsylvania, focusing on brain networks and cognition in the group of Dani Bassett. In 2015, she received her doctorate in physics from the Massachusetts Institute of Technology, where she worked with Xiao-Gang Wen on novel topological states in quantum electronic systems. She holds a master of philosophy from the University of Cambridge and a bachelor of science from Yale University. She is a recipient of the Simons-Berkeley Research Fellowship, the Africk Family Postdoctoral Fellowship, and the Gates Cambridge Scholarship. 

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