We are delighted to share that the next Royal Statistical Society North Eastern local group event will be a talk by Prof Miltos Alamaniotis (University of Texas at San Antonio).
Abstract:
The terrorist attacks of 9/11 led to a redefinition of security architecture and its priorities to prevent such acts. One emerging scenario involves the potential use of nuclear materials for attacks in metropolitan areas, resulting in severe and widespread consequences. Typically, detecting and identifying terrorist activities involve sensors that measure radiation and analyze the data for significant patterns. Recent advancements in Artificial Intelligence across various fields have shown promise in addressing critical challenges in nuclear security pertaining to data analytics. Specifically, AI-empowered software modules embedded in radiation sensors — creating smart sensors — allow for real-time data analysis and decision-making. AI facilitates high-definition, high-speed analysis of diverse data types directly within the sensors, improving the monitoring of nuclear materials' use, storage, and transport. Additionally, the embedded AI software in sensors eliminates the need for human operators to have expertise in nuclear physics and engineering. This talk will explore the AI solutions in data analytics pertaining to isotope recognition and background modeling — e.g., the Nobel Awarded Hopfield Neural Networks and the Matrix Profile background method — as well as challenges, and societal and ethical concerns related to using AI-empowered software in sensors to upgrade security measures and prevent a nuclear 9/11 in metropolitan areas.
Dr. Miltos Alamaniotis, University of Texas at San Antonio
Associate Professor and the Lutcher Brown Endowed Fellow in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA). Before joining UTSA, he worked as a researcher at Purdue University. He received his BS in Electrical and Computer Engineering from the University of Thessaly, 2005, and MS and PhD in Nuclear Engineering with an emphasis in Applied Artificial Intelligence from Purdue University in 2010 and 2012, respectively. His interdisciplinary research focuses on the development of Artificial Intelligence and machine learning approaches applied to intelligent energy systems, nuclear power systems, and nuclear security to detect hidden radioactive materials. He has published over two hundred fifty (250) research papers in scientific journals, books and proceedings of international conferences. He serves as Associate Editor in the International journal on Artificial Intelligence Tools, Internet of Things (Elsevier), and has served as the Program Chair in IEEE International Tools with Artificial Intelligence in 2018 and 2020. He had been an external researcher at Argonne National Laboratory (Illinois, USA) from 2010 to 2012, as visitor in the Energy and Power Systems group at Oak Ridge National Laboratory (Tennessee, USA) in May 2016, while he was a member of the first academic team visiting the Nevada National Security Site to get radiation measurements. He is the recipient of the Distinguished Alumnus Award of the Department of Electrical and Computer Engineering. University of Thessaly in July 2017, and the Presidential Award for Distinguished Research Achievements at UTSA in 2022. In 2023, the National Academy of Engineering included him in the “top-notch 100 Early Career Engineers in USA” for the 2023 Frontiers-of-Engineering Symposium.
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