Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 419 - Section on Risk Analysis Student Paper Award Session
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #322994
Title: Tlife-GDN: Detecting and Forecasting Spatio-Temporal Anomalies via Persistent Homology and Geometric Deep Learning
Author(s): Zhiwei Zhen* and Yuzhou Chen and Ignacio Segovia Dominguez and Yulia Gel
Companies: University of Texas at Dallas and Princeton University and Unviersity of Texas at Dallas and University of Texas at Dallas
Keywords: Anomaly detection; Geometric deep learning; Presistent homology ; Covid-19
Abstract:

Abstract: Most recently, the tools of geometric deep learning (GDL) and, in particular, graph neural networks emerge as a promising new alternative in unsupervised anomaly detection problems where the data exhibit a sophisticated nonlinear dependence structure such as various geospatial surveillance systems. However, prevailing GDL-based methods for anomaly detection tend to exhibit limited capabilities to capture multi-scale spatio-temporal variability which is ubiquitous in many applications, particularly, related to biosurveillance and biothreats. Motivated by the problem of assessing COVID-19 severity, we develop a novel approach to unsupervised anomaly detection in spatio-temporal data by fusing the notion of GDL with the emerging direction of persistent homologies and topological data analysis. In particular, our key idea is to bolster the GDL performance by leveraging the complementary insight on the intrinsic multi-scale data organization which topological descriptors can provide. We show the utility of the new approach to detecting, forecasting and interpreting risks in COVID-19 clinical severity, measured in terms of hospitalization rates, in three U.S. states.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program