Guy Perry, SMIEEE, PE, CEO of BluOcean AI

Introduction

Biography:

I split my time between building BluOcean.ai (a Generative AI Agent Platform for business excellence) and advising Fortune 500 clients as a Principal Consultant. Over 30 years, I’ve optimized operations and led supply-chain transformations across Manufacturing, Financial Services, Healthcare and beyond. Today, I unite my product-company insights with hands-on consulting to deliver measurable Return on Investment (ROI).

Hulya Kirkici, Ph.D., Chair of Department of Electrical and Computer Engineering, University of South Alabama

IEEE Region 3 Fellow Coordinator

Biography:

Hulya Kirkici is Professor and the Department Chair of Electrical and Computer Engineering at the University of South Alabama. She received B.S. and M.S. in physics from Middle East Technical University (METU), Turkey; and Ph.D. in electrical engineering from Polytechnic University (currently NYU), NY.  Previously, Dr. Kirkici was Professor of electrical and computer engineering at Auburn University (1992-2016), visiting scholar / consultant and Summer Faculty Fellow at the Air Force Research Laboratory – Wright Patterson Air Force Base, (2014-2015), and visiting scientist/engineer at NASA, Marshall Space Flight Center, Huntsville, AL (1999-2000).

Dr. Kirkici is a Fellow of IEEE for “contributions to high frequency, high field dielectric breakdown and electrical insulation for space and aerospace power systems” (class of 2017), a recipient of the IEEE Eric O. Forster Distinguished Service Award, the highest honor given by the Dielectrics and Electrical Insulation Society (2015), IEEE William G. Dunbar Award “for continuing contribution to high-voltage and high frequency insulation research and engineering education,” (2014) and a recipient of the IEEE Sol Schneider Award “for continuing technical and administrative leadership in the power modulator and high voltage communities,” (2010).

Prabhakar Pathak, Ph.D., Professor Emeritus of Ohio State University

This talk provides a concise introduction to the uniform theory of diffraction (UTD), a modern ray-based method utilized for the analytical prediction of electromagnetic (EM) radiation from antennas, especially in the presence of complex platforms. UTD serves as a valuable tool in numerous advanced antenna applications, where the platforms are often electrically large relative to the wavelength of the EM waves. Such scenarios commonly arise in aerospace, including airborne, UAV, missile-borne, shipboard, and satellite-mounted systems, as well as other related fields.

Advantages of the UTD Ray Method

The primary advantage of the UTD ray method is its ability to deliver a clear and intuitive physical understanding of high-frequency EM wave propagation. UTD accomplishes this by describing antenna radiation through an appropriate class of rays, which interact with the surrounding platform and ultimately reach a distant observer. No other existing method offers such a direct and straightforward physical interpretation for antenna radiation in environments complicated by platform structures.

Comparison with Numerical Methods

At lower to moderately high frequencies, rigorous numerical computer-based solutions are available for antenna analysis. These solutions are formulated in a self-consistent manner and can effectively solve complex problems. However, while these numerical approaches are viable, they fail to provide insight into the underlying physical mechanisms of radiation. Furthermore, commercial codes that rely on these
numerical solvers become increasingly impractical and computationally intractable at higher frequencies.

Utility of UTD at Moderate to High Frequencies

The UTD method offers a relatively simple and rapid approach for addressing antenna performance issues at moderate to high frequencies. Its efficiency and clarity make it particularly suitable for practical applications, including airborne and UAV platforms. Throughout this document, various examples will demonstrate the accuracy and simplicity of UTD in real-world scenarios.

Biography:

Dr. Pathak received the Ph.D. degree in electrical engineering from The Ohio State University (OSU), Columbus, OH, USA, in 1973. He is currently a Professor (Emeritus) at OSU and an Adjunct Professor at the University of South Florida, Tampa, FL, USA. He is regarded as a Co-Developer of the uniform geometrical theory of diffraction (UTD). His research continues in the development of new UTD ray solutions in both frequency and time domains, as well as in the development of fast beam and hybrid methods, for analyzing large electromagnetic scattering and antenna problems (such as reflector systems and conformal phased arrays). He has authored/co-authored over a hundred journal and conference papers, as well as contributed chapters to seven books. His current research interests include the development of analytical tools for the prediction of radiation and coupling associated with antennas/arrays placed on large airborne, spaceborne, or other complex platforms of engineering interest. In addition, he was involved in novel methods related to near field antenna measurements.

Issam El Naqa, Ph.D., DABR, FAAPM, FIEEE, FAIMBE, Founding Chair of Department of Machine Learning, Moffitt Cancer Center

Artificial Intelligence (AI) is a transformative technology that is capturing the popular imagination and can revolutionize biomedicine. AI and Machine Learning (ML) algorithms have the potential to break through existing barriers in healthcare research and practice such as automating workflow processes, personalizing care, and reducing disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use.

In this presentation, we will present some of the promising AI/ML applications and highlight potentials and current challenges for clinical translation. The role of AI education for specialized applications will be also discussed. We will further show examples of implementing new approaches to overcome these challenges in Oncology from and discuss their implications for the future of AI/ML in the field.

Biography:

Dr. El-Naqa is the founding chair of the Department of Machine Learning at Moffitt Cancer Center and Professor in oncological sciences Tampa, Florida.  He is a certified Medical Physicist by the American Board of Radiology. He is a recognized authority in the fields of Machine Learning, Data Analytics, and Oncology Outcomes Modeling and has published extensively in these areas with more than 290+ peer-reviewed journal publications and 5 edited textbooks. He has been a member and Fellow of several academic and professional societies, including AAPM, IEEE, AIMBE, BIR. His research has been funded by several federal and private grants in Canada and USA and served on national and international study sections. He acts as a peer reviewer and editorial board member for several leading international journals in his areas of expertise.

Biography:

Biography:

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