PLENARY/KEYNOTE SPEAKERS
Plenary / Keynote/ Invited Speakers
Plenary Title: Advancing radiology with AI: The rise of vision-language models for report generation
Abstract:
The field of radiology is undergoing a transformative shift driven by artificial intelligence, particularly with the emergence of powerful vision-language models (VLMs). As the demand for radiological services continues to rise, coupled with a global shortage of trained radiologists, the pressure to deliver accurate, timely, and consistent diagnostic reports has never been greater. This plenary talk explores how AI, especially transformer-based architectures and large vision-language models, is revolutionizing radiology report generation. By automatically interpreting medical images and generating coherent, clinically relevant text, these models hold the promise of reducing routine workload, accelerating diagnosis, and enhancing decision support for healthcare providers. We will present a comprehensive overview of key developments, showcasing advances in model design, training strategies, and evaluation frameworks. Real-world datasets and benchmarks will be discussed, along with comparative performance analysis of leading systems. The session will conclude with critical reflections on current limitations and offer forward-looking perspectives on how AI can be more deeply integrated into radiological workflows making radiology not only faster but also smarter.
Biography:
Prof. Essam Rashed is a researcher in the field of medical artificial intelligence, with a focus on medical image analysis, large language models, and multimodal learning. He is currently based in Japan, where he contributes to various national and international research initiatives aimed at improving diagnostic workflows using cutting-edge AI technologies. Dr. Rashed’s work explores the intersection of vision-language models, radiological report generation, and clinical decision support systems. He has authored several publications on medical image analysis and data science, and actively participates in global collaborations bridging academia, healthcare, and AI.
Keynote Title: What makes Cognitive Radio Networks Cognitive?
Abstract:
At the core of cognitive radio networks is the promise of intelligent, flexible spectrum sharing — a shift from static allocation to dynamic, need-based access. But what exactly endows a radio network with cognition? This talk explores how the cognitive aspect lies in the network’s ability to observe its surroundings, interpret ongoing spectrum usage, and make independent decisions about when and how to transmit without causing harmful interference. Spectrum sharing becomes the practical expression of cognition, demanding awareness of other users, prediction of availability, and real-time coordination. We will examine how machine learning methods, from predictive modeling to multi-agent reinforcement learning, are enabling radios to negotiate spectrum access intelligently and adapt to shifting conditions. The session will also explore implementation challenges, including fairness, latency, regulatory compliance, and coexistence with legacy systems. Ultimately, this keynote seeks to answer what transforms a traditional radio into a cognitive one, and why spectrum sharing is the most visible manifestation of that transformation.
Biography:
Dr. Manish Wadhwa is the Chairperson and Professor in the Department of Computer Science at Salem State University. He assumed the department chair role in July 2022 and oversees faculty, staff, budget, and numerous administrative operations within the department. Before this leadership role, he served for many years as the coordinator for the Information Technology (IT) program and chaired several IT-related committees, contributing significantly to the academic and operational growth of the department.
Since joining Salem State University in September 2013, Dr. Wadhwa has been deeply involved in the planning, development, and successful launch of a new IT major. Approved in 2018, this program has since seen notable growth under his guidance. As an educator, Dr. Wadhwa teaches a wide array of courses in both Computer Science and Information Technology, bringing his research and professional expertise into the classroom. A cybersecurity minor and a certificate were also developed under his leadership.
Before his tenure at Salem State University, Dr. Wadhwa served as Program Director of Information Technology at South University in Virginia Beach, VA. From July 2011, he led the Department of Information Technology within the College of Business, managing associate, bachelor’s, and master’s degree programs. He also taught a variety of IT courses and played a pivotal role in managing faculty and academic operations related to the IT curriculum.
Dr. Wadhwa’s research spans interdisciplinary areas, including Cognitive Radio Networks, Dynamic Spectrum Sharing, Biofield Analysis, Body Area Networks, and Brain-Computer Interfaces. He has published in numerous prestigious journals and conferences and has served as a reviewer, editor, and editorial board member for several academic venues. Additionally, he was an editor of an interdisciplinary book bridging Information Technology and Business – “Technology, Innovation, and Enterprise Transformation.”
With a strong record of academic leadership, innovative curriculum development, and impactful research, Dr. Wadhwa continues to be a distinguished contributor to the fields of Computer Science and Information Technology.
Keynote Title: Machine Learning for Human Interaction
Abstract:
Robust human interaction requires the ability, at least in part, to
infer the thoughts and desires of others. We will discuss our recent
work to improve AI automated reading of human preferences, percepts, and
desired communications. We show how state-of-the-art facial expression
analysis can be used to infer pain levels and preferences. We also
show how we can leverage foundation models to extract realistic estimates
of viewed images and decode imagined handwriting.
Biography:
Virginia de Sa is a professor of Cognitive Science, HDSI Chancellor's
Endowed Chair, and associate director of the Halicioglu Data Science
Institute at UC San Diego. She received a B.Sci. (Engineering) from Queens University in Ontario, and a PhD in Computer Science (machine learning) from the University of Rochester before receiving postdoctoral training in machine learning with Geoff Hinton at the
University of Toronto and in neuroscience, with Michael Stryker and Michael Merzenich, at UCSF. She is a recipient of a Sony Faculty Innovation Award, a UCSD Chancellor’s Collaboratories Award, Kavli Innovative Research Award, NSERC 1967 Science and Engineering
Scholarship, NSERC postdoctoral fellowship, Sloan Postdoctoral fellowship and NSF CAREER award. Her research goal is to better understand the neural basis of human perception and learning, both
from a neural and computational point of view. She investigates the computational properties of machine learning algorithms and what physiological recordings and the constraints and limitations of human
performance tell us about how our brains perceive and learn. These and related findings are used to develop computational models, improve machine learning algorithms, and develop EEG-based brain-computer interfaces. Her lab also uses visual illusions and technology from
brain-computer interfaces to pique student interest in science and engineering; activities include playing with visual illusions, seeing their own brain activity and competing with fellow students in brain-control games.
Mr Chris Katsaropoulos
Elsevier, USA
Keynote Title: Publishing Your Machine Learning and Data Science Research with Elsevier
Abstract:
A brief and informative look at how to have your research on Machine Learning and Data Science published with Elsevier. Learn about new publications and areas of
research interest, and how to have your work published in an Elsevier journal, or an Elsevier book. We will discuss the publishing process, along with any questions
you may have about getting started.
Biography:
Chris Katsaropoulos is Senior Acquisitions Editor for Computer Science at Elsevier. He has been commissioning books and publishing in the Computer Science
field for more than thirty-five years, with a broad range of major publishers. His Computer Science list at Elsevier covers AI/ML and associated areas of Computational
Intelligence, Data Science,
Software Engineering, Information Security, and Computational Modeling.
