TALK TITLE: Machine Learning in Energy Harvesting Cooperative Communication Systems

Leveraging energy harvesting technology in conjunction with cooperative communications substantially enhances the efficiency of wireless communication systems. This efficiency is further improved when multiple relays are employed. However, common challenges such as relay selection, scheduling, and synchronization become more complex due to the rapidly changing channel characteristics and the stochastic nature of energy harvesting processes.
These challenges have been extensively addressed in the literature, spanning various scenarios with diverse objectives, such as maximizing secrecy performance, minimizing outage probability, mitigating interference, and maximizing throughput. While many proposed algorithms approach near-optimal or optimal performance, they frequently entail high complexity, extended run-time, or the need for additional information. Recently, machine-learning techniques have emerged as promising solutions for these challenges, applicable to both energy harvesting and conventionally battery-powered systems. These techniques yield performance ranging from moderate to optimal, and offer resilient, low-complexity solutions.
This talk will address the common challenges encountered in energy harvesting wireless sensor networks, particularly in relay selection, scheduling, and synchronization, along with proposed solutions. It will also explore the advantages of integrating machine learning into the system, alongside discussions on the related challenges and potential future directions.

Dr. Ahmed Ammar holds master’s and Ph.D. degrees in electrical engineering from West Virginia University. Currently, he serves as an assistant professor in the Department of Electrical and Computer Engineering and Computer Science at Ohio Northern University. His research focuses on energy harvesting communication systems and high-precision hardware design. Dr. Ammar has secured funding for his research endeavors and has collaborated with various professionals in the field. Notably, one of his research contributions has been honored with the Best Paper Award. He actively participates in organizing committees for several conferences and is deeply engaged in educational research and activities. Recently, he co-led an education workshop at Ohio Northern University and was invited as a speaker at the KEEN national conference.

TALK TITLE: AI Applications for Signal/Image Processing and Computing: Personalized Medicine

Dr. Fahmi Khalifa, Ph.D. is an Assistant Professor of ECE, Morgan State University (MSU). He received his BS and MS degrees in Electronics and Communication Engineering from Mansoura University, Egypt in 2003 and 2007, respectively, and his PhD degree in 2014 from Electrical and Computer Engineering (ECE) Department, University of Louisville (UofL), USA. Dr. Khalifa has more than 15 years of hands-on experience in the fields of artificial intelligence, image/signal processing, machine learning, biomedical data analysis, and computer-aided diagnosis with more than 180 publications appearing in top-rank journals and international conferences in addition to and five US patents. Dr. Khalifa is an associate editor for IEEE Access, IEEE JBHI and Frontiers in Neuroscience; guest edited multiple special issues; and serves as a reviewer for 50+ journals and conferences. Dr. Khalifa’s honors and awards include Mansoura University scholarship for distinctive undergraduate students for four consecutive years (1999–2002), Theobald Scholarship Award (ECE, UofL,2013), the ECE Outstanding Student award for two times (ECE, UofL, 2012 and 2014), the John M. Houchens award for the outstanding dissertation (UofL, 2014), the second-place Post-Doctoral Fellow award in Research! Louisville (UofL, 2014), PowerLIVE Award for Faculty commitment to students and their academic success (MSU, 2023), and Final list for the “Instructional Innovator of the Year” (MSU, 2023).

TALK TITLE: Tech-Forward Smart Cities: Leading the Way into the Future

In our dynamic world, the concept of smart cities has gained significant traction, driven by technological advancements aimed at enhancing urban living and effectively managing the influx of data from various sources. This discussion introduces an innovative framework designed to tackle the intricate data processing challenges inherent in smart city environments.
At the heart of this framework lies edge computing, which processes data closer to its origin, reducing latency and bandwidth requirements and enhancing overall efficiency in smart city data processing. The framework also employs intelligent agents on edge nodes to analyze and process data before sending only pertinent information to the cloud for further processing. This distributed approach optimizes resource usage and ensures quick responses to city events.
Additionally, the framework's cloud component provides scalable storage and computing resources for handling large data volumes. Deep learning models in the cloud analyze aggregated data to extract insights and make informed decisions. One of the framework's main advantages is the automation enabled by intelligent agents, which can independently perform tasks based on predefined functions. This automation enhances city operations efficiency infrastructure responsiveness and adaptability.
This framework offers a comprehensive method for utilizing edge and cloud computing with intelligent agents to efficiently process data, make decisions, and automate processes in smart cities. Embracing this framework enables cities to use technology to create safer, more sustainable, and more livable urban environments for all residents.

Dr. Rasha Gargees is an Assistant Professor in the Department of Computer Science at Central Michigan University. She received her Ph.D. in Computer Science from the University of Missouri, where she also completed master’s degrees in the same field. Following her doctoral studies, Dr. Gargees served as a Postdoctoral Fellow at the University of Missouri. Her research interests encompass smart cities, cloud and fog computing architectures, artificial intelligence, machine learning, parallel and distributed computing, medical diagnoses, geospatial intelligence, Internet of Things (IoT), computer networks, and big data. She actively participates in program and organizing committees for prestigious venues. Dr. Gargees has been recognized with several accolades, including the PFFFD Postdoctoral Fellowship, the HCED Scholarship, the Best Presenter Award at IEEE CCWC 2020, the 1907 Women in Engineering Award, and Recognized Reviewer Certificates from leading journals.

TALK TITLE: Leveraging AI to Enhance Critical Infrastructure Security

Cyber-attacks on critical infrastructure are on the rise globally, including attacks on energy, water, healthcare, and transportation systems. These attacks threaten everything from everyday life to global security and economy. AI and Machine Learning have had significant impact on cybersecurity and critical infrastructure security. This talk will highlight challenges in critical infrastructure security how AI and Machine Learning methods could be leveraged to strengthen critical infrastructure security. The speaker will address AI and ML adoption, risk management, and best practices, as well as the impact on the workforce and future trends.

Dr. Eman El-Sheikh is Associate Vice President and Professor at the University of West Florida Center for Cybersecurity. Eman has extensive expertise and 30 years of experience in AI, Machine Learning, and Cybersecurity education, research, and workforce development. She won over $26 Million in competitive grants and contracts, and several awards, including 2024 GISEC Global Educator of the Year, induction into the UWF Million Dollar Research Hall of Fame, and 2020 Women Leaders in Cybersecurity by Security Magazine. Dr. El-Sheikh leads several national initiatives, including the National Cybersecurity Workforce Development Program, CyberSkills2Work, and received numerous grants to enhance cybersecurity education, workforce development and capacity building. She launched the Cybersecurity for All® Program to enhance competencies and hands-on skills for evolving cybersecurity work roles. The program was recognized among the 2020 Innovations in Cybersecurity Education.
Dr. El-Sheikh has published several books, including Computer and Network Security Essentials by Springer Publishing, over 100 peer-reviewed articles, and given over 150 keynote and invited talks. She teaches and conducts research related to the development, use, and evaluation of Artificial Intelligence and Machine Learning for Cybersecurity. Eman founded the Florida Women in Cybersecurity Affiliate and serves on its Advisory Board. She holds an M.S. and Ph.D. in Computer Science from Michigan State University, and a B.S. in Computer Science from the American University in Cairo.

TALK TITLE: Global Fiberization Trend and Huawei Innovative Solutions

Mr. Ahmed Youssef is working as Fixed Access Network Solution sales Manager for Huawei Egypt as he is a distinguished expert in the access network domain at Huawei, with over 9 years of impactful experience in the telecom field . His deep technical expertise and strategic insights have propelled numerous high-profile projects, establishing him as a key player in the industry. Complementing his engineering prowess, Youssef holds an MBA in Marketing, which enhances his ability to align technological innovations with market needs and business goals. His unique blend of skills enables him to craft and implement solutions that drive growth and customer satisfaction. At Huawei, Youssef is renowned for his visionary approach, leadership, and commitment to excellence, consistently pushing the boundaries of what's possible in access network technology.