Dear Colleagues,
IISE/QCRE division would like to invite you to attend the webinar on Friday Dec. 11, 2020, 4-5 p.m. Eastern Time. If interested, please register through the link below:
https://zoom.us/meeting/register/tJEtcumvpzssH9CRnISoLZYjx3Kondv9obtZ
This event is free but advance registration is mandatory.
Title: New Frameworks for Anomaly Detection in Large-Scale Power Distribution Networks
When: 4 - 5 PM EST, Friday, Dec. 11, 2020
Presenter: Dr. Ramin Moghaddass, Assistant Professor, Department of Industrial Engineering, University of Miami
Abstract: Power distribution networks are continuously monitored by various sensors that are placed at a subset of nodes and edges. The multidimensional data observed from these networks over time build large-scale graph data with highly dependent data points. Monitoring large-scale attributed networks with thousands of nodes and heterogeneous sensor data to detect anomalies and unusual events is a complex and computationally expensive process. This work introduces a new generic approach for network anomaly detection that can utilize the information from the network topology, the node attributes (sensor data), and the anomaly propagation paths in an integrated and intelligent manner to monitor the entire network. Experimental results demonstrate the superior performance of the framework compared to traditional machine learning approaches.
Bio: Dr. Ramin Moghaddass is an Assistant Professor of Industrial Engineering with a secondary appointment in Management Science from Miami Herbert Business School at the University of Miami. He is the Director of the Data Analytics Lab and Industrial Assessment Center at the University of Miami. His research centers around advanced data analytics and stochastic control in energy systems and network structures. His research has received several federal and non-federal grants/awards, such as the National Science Foundation's Faculty Early Career Development Award (CAREER), Amazon AWS Machine Learning Research Award, INFORMS/QSR Data Challenge Competition Award (Winner), and INFORMS/Data Mining Best Paper Award (Runner-up).
Regards,
Mingyang Li, Ph.D., University of South Florida
Yisha Xiang, Ph.D., Texas Tech University
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Mingyang Li
Assistant Professor
University of South Florida
Tampa FL
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