Dr. Tang is currently a group leader at the MPI for Dynamics and Self-Organization in the Living Matter Physics department. She completed her doctoral work in condensed matter theory at MIT and then worked as a postdoctoral fellow in the field of complex systems with Dr. Danielle Bassett at the University of Pennsylvania. During her postdoctoral work, she focused on network dynamics and controllability, brain development and effective learning. Her group focuses on information flow in living systems that promotes the emergence of fascinating phenomena such as cognition and brain development. Some examples include the control of dynamics in brain networks: how the topology of white matter changes across development, and the geometry of neural activity during effective learning. They develop analytical models guided by empirical observations, in order to bridge between microscopic constituents and the macroscopic emergent phenomena that govern our daily life.

Her talk will focus on emergent function in dynamic neural systems such as development and cognition. A fundamental cognitive process is to map value and identity onto the objects we learn about. However, what space best embeds this mapping is not completely understood. They develop tools to quantify the space and organization of such a mapping in neural responses as reflected in functional MRI, to show that quick learners have a higher dimensional representation than slow learners, and hence have more easily distinguishable whole-brain responses to objects of different value. Furthermore, they find that quick learners display more compact embedding of their neural responses, which is consistent with greater efficiency of cognitive coding. She will also discuss quantifying information in fluid flows, such as can be measured in brain ventricles.