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Duong-Tran to present at WMU

Duong-Tran to present at WMU

Photo of Duy Duong-Tran
Duy Duong-Tran
Second-year PhD student Duy Duong-Tran will present at seminar at Western Michigan University on Apr 11.

Title: "From Human Brain Fingerprints to Topological Morphospace & Beyond"

Network science is the research area that applies graph theory to unravel complex relationships among elements, i.e. nodes in a system, i.e. graph/network. These interactions can be modelled through pairwise interactions, i.e. edges. Ever since the birth of social media (Facebook, LinkedIn, etc.), networks have been extensively studied in both academic and industrial setting.

I will discuss the recent extension of network science in an emergent research area called Network Neuroscience in which the brain can be modelled as a combinatorial object, graph-theoretically, called the connectome. I will then discuss recent advancements in Network Neuroscience through the so-called “brain fingerprints.” In the 17th century, physician Marcello Malpighi observed the existence of patterns of ridges and sweet glands on fingertips. The next challenge for human identifiability is posed on brain data, particularly brain networks, both structural and functional. Specifically, I will briefly discuss the procedure used to uncover individual fingerprints of a connectome (as represented by a network/graph). Such procedure is based on the network reconstruction using group-wise decomposition in a finite number of brain connectivity modes.
In the second part of the seminar, I will discuss the application of a mathematical tool called network morphospace to understand how the brain (re-)configures itself to meet various cognitive demands from external environment. I will show that human brain, as a complex system, accomplishes these goals economically. Pre-prints for this project is available at:

Duong-Tran is the second-year Ph.D. student in the school of Industrial Engineering at Purdue University. His research interest lies in the intersection of computational neuroscience, network science and topological data analysis.