Seminar: Special seminar - Faculties of Electrical and Computer Engineering, Computer Science and Biomedical Engineering

ECE Women Community

Optimal Mass Transport and the Robustness of Complex Networks

Date: June,01,2022 Start Time: 11:30 - 12:30
Location: 1003, Meyer Building
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Lecturer: Professor Allen Tannenbaum
Affiliations: Distinguished Professor of Computer Science and Applied Mathematics, Stony Brook University,
and
(Affiliate) Attending, Medical Physics, Memorial Sloan Kettering Cancer Center
Research Areas:

A major problem in data science is representation of data so that the variables driving key functions can be uncovered and explored. Correlation analysis is widely used to simplify networks of feature variables by reducing redundancies, but makes limited use of the network topology, relying on comparison of direct neighbor variables. The proposed method incorporates relational or functional profiles of neighboring variables along multiple common neighbors, which are fitted with Gaussian mixture models and compared using a data metric based on a version of optimal mass transport tailored to Gaussian mixtures. Hierarchical interactive visualization of the result leads to effective unbiased hypothesis generation. In a cancer gene expression study, this method uncovered an unanticipated immunosuppressive mechanism resembling maternal–fetal immune tolerance.

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