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<b><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0">Title: Graph Data Analytics for Secured and Reliable Energy Systems. </span></b><o:p class="ContentPasted0"> </o:p></p>
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<b><i><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0">Abstract:</span></i></b><i><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0"> Modern energy systems with
massive integration of renewable energy resources, high-resolution sensors, and advanced measurement devices are more susceptible to cyber-attacks and physical stresses due to their increased complexity and exposure to external systems and stakeholders. Security
and reliability of energy systems are, therefore, crucial for maintaining operational integrity and providing seamless electricity to customers. Modeling the complex energy systems as graphs allows for the incorporation of topology, interconnection, and inter-relation
among the system components into the data analysis for enhancing situational awareness in energy systems. In this presentation, the applications of two powerful graph data analytic tools: graph signal processing (GSP) and graph neural network (GNN) will be
discussed to show their effectiveness in addressing several security and reliability-related problems: 1) detection, localization, and characterization of cyber-attacks, energy theft, and physical stresses in smart grids and advanced metering infrastructures,
2) recovery, estimation, and prediction of system states, and 3) optimum meter placement in the systems. This presentation covers both accomplished work and future research directions, including extending GSP and GNN to other networked systems beyond energy
systems, e.g., smart cities connecting smart grids, smart transportation systems, and internet-of-things. </span></i><o:p class="ContentPasted0"> </o:p></p>
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<b><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0">Biography:
</span></b><span style="font-family: "Times New Roman", serif; color: black;" class="ContentPasted0">Md Abul Hasnat received the Ph.D. degree in Electrical Engineering from the University of South Florida, Tampa, FL, USA, in 2023. Currently, he is a Post-doctoral
Research Associate with the Systems and Information Engineering Department at the University of Virginia, Charlottesville, VA, USA. He received the B.Sc. degree in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology,
Dhaka, Bangladesh, in 2014, and the M.Sc. degree in Electrical Engineering from the University of South Florida in 2020. His research interests include signal processing, data analytics, machine learning, and network science in application to the security
and reliability of energy systems and other critical cyber�physical infrastructures. </span><o:p class="ContentPasted0"> </o:p></p>
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<b><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0">Title: Graph Data Analytics for Secured and Reliable Energy Systems. </span></b><o:p class="ContentPasted0"> </o:p></p>
<p style="margin:0in;font-size:12pt;font-family:"Aptos", sans-serif"><b><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0"> </span></b><o:p class="ContentPasted0"> </o:p></p>
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<b><i><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0">Abstract:</span></i></b><i><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0"> Modern energy systems with
massive integration of renewable energy resources, high-resolution sensors, and advanced measurement devices are more susceptible to cyber-attacks and physical stresses due to their increased complexity and exposure to external systems and stakeholders. Security
and reliability of energy systems are, therefore, crucial for maintaining operational integrity and providing seamless electricity to customers. Modeling the complex energy systems as graphs allows for the incorporation of topology, interconnection, and inter-relation
among the system components into the data analysis for enhancing situational awareness in energy systems. In this presentation, the applications of two powerful graph data analytic tools: graph signal processing (GSP) and graph neural network (GNN) will be
discussed to show their effectiveness in addressing several security and reliability-related problems: 1) detection, localization, and characterization of cyber-attacks, energy theft, and physical stresses in smart grids and advanced metering infrastructures,
2) recovery, estimation, and prediction of system states, and 3) optimum meter placement in the systems. This presentation covers both accomplished work and future research directions, including extending GSP and GNN to other networked systems beyond energy
systems, e.g., smart cities connecting smart grids, smart transportation systems, and internet-of-things. </span></i><o:p class="ContentPasted0"> </o:p></p>
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<b><span style="font-family: "Times New Roman", serif; color: rgb(14, 16, 26);" class="ContentPasted0">Biography:
</span></b><span style="font-family: "Times New Roman", serif; color: black;" class="ContentPasted0">Md Abul Hasnat received the Ph.D. degree in Electrical Engineering from the University of South Florida, Tampa, FL, USA, in 2023. Currently, he is a Post-doctoral
Research Associate with the Systems and Information Engineering Department at the University of Virginia, Charlottesville, VA, USA. He received the B.Sc. degree in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology,
Dhaka, Bangladesh, in 2014, and the M.Sc. degree in Electrical Engineering from the University of South Florida in 2020. His research interests include signal processing, data analytics, machine learning, and network science in application to the security
and reliability of energy systems and other critical cyber�physical infrastructures. </span><o:p class="ContentPasted0"> </o:p></p>
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