Biography: Dr. David J. Livingston took office as Lewis University’s 10th President on July 1, 2016 with more than 15 years of academic and leadership experience. Over the last seven years Lewis has added new health science programs in Speech Language Pathology and Occupational Therapy as well as seen significant growth in Aviation and Computer Science. Lewis has also had significant growth in international students with over one thousand international students now studying in Romeoville.
Dr. Livingston previously served as President of Lourdes University (2013-2016). During his tenure, Lourdes experienced an increase in the quality, retention and graduation rates of its students; expanded academic programs and renovated the campus to support their strategic growth initiatives.
Prior to his presidency at Lourdes University, he served Mercyhurst University for 16 years in various capacities, including President of the Faculty Senate and Vice President for Advancement. Prior to joining the Mercyhurst community, he taught religious studies at Loyola Academy High School in Wilmette, Vanderbilt University in Nashville, University of St. Francis in Joliet, Joliet Catholic Academy and Loyola University of Chicago.

Title: Considerations on the Impact of Data science, Artificial Intelligence Augmented Reality on the Future of Higher Education and Society
Abstract: In the US spending on post-secondary education exceeds $700 Billion US dollars, globally that number is well over $3 Trillion. Higher education, like all other industry, is deeply impacted by the fast pace of change in computing, especially in reference to Generative Artificial Intelligence, Virtual and Augmented reality and the role of big data in our society. This lecture will focus on the opportunities and challenges faced by higher education as an industry in the coming decades due to these significant shifts in computing potential. Nearly every area within higher education will feel the impact from recruiting, pedagogy, research, and student life. There is great opportunity, but the risks must be acknowledged and managed carefully.

---

Biography: Alexandre Muzy is CNRS research fellow at Université Côte d'Azur (I3S computer science laboratory). He is co-director of the NeuroMod institute and in charge of the Modeling, Simulation & Neurocognition (MS&N) research group. He is a specialist of computational modeling and simulation based on system theory, more specifically discrete event systems currently applied to neurocognitive systems, with more than 70 international research publications. He created the computational activity paradigm for structuring models and developed with Bernard P. Zeigler the computational iterative system paradigm. The latter paradigm has been used as a new foundation of the Theory of modeling and simulation - (3d edition). Based on the mapping from in vivo neurocognitive activities to temporal computations, he works on the specification of the computational neurocognitive system (cf. Computabrain project) at learning, modeling and simulation levels.

Title: Enlightening into brain’s frugality: Interplay of Computational and Neural Coordination
Abstract: The brain remains by far the most frugal computing machine, in terms of both computational complexity and memory. In the theory of activity-based modeling and simulation, the coordination of interactions between subsystems can be modeled computationally to simulate overall system activity. In theoretical neuroscience, different temporal hypotheses exist about the coordination of electrical interactions between neurons to understand overall brain activity. In this talk, I will question how the computational coordination mechanisms between subsystems, formulated in the theory of activity-based modeling and simulation, can enlighten neural coordination mechanisms at brain scale. This crossover study opens the way to scalable simulations of the brain, and, hopefully, new software and hardware brain implementations.

---

Biography: Roger L. King received the B.S. degree from West Virginia University, Morgantown, WV, USA, in 1973, and the M.S. degree from the University of Pittsburgh, Pittsburgh, PA, USA, in 1978, both in electrical engineering, and the Ph.D. degree in engineering from the University of Wales, Cardiff, U.K., in 1988. He began his career with Westinghouse Electric Corporation, but soon moved to the U.S. Bureau of Mines Pittsburgh Mining and Safety Research Center, Pittsburgh, PA, USA. Upon receiving his Ph.D. in 1988, he accepted a position in the Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA. He retired from Mississippi State University in 2018 as a Giles Distinguished Professor. He has authored more than 500 journal and conference publications and holds 4 patents. Over his career he has served in a variety of leadership roles with the IEEE Industry Applications Society, Power Engineering Society, and Geosciences and Remote Sensing Society, NATO, NASA, USDOT, and a variety of other organizations at the state and local level. He has received numerous awards for his leadership in research including the Department of Interior’s Meritorious Service Medal, named as an Honorary Professor at Cardiff University in the United Kingdom, a Fellow of the Learned Society if Wales, and a Life Fellow of IEEE.

Title: That was then, this is now
Abstract: This presentation will give a perspective of the speakers 4-decade journey through Artificial Intelligence. Beginning with a PhD dissertation using Knowledge Based Expert Systems, examples of multiple projects and students along the journey using different AI tools and techniques, and end with some thoughts on the future. Topics to be discussed will be the evolution from experiential based AI techniques to data-based systems. Why is there such an emphasis on data-based approaches today when the algorithms we use today have been around for decades? What are the advantages and disadvantages of the approaches? How has the definition of big data evolved over the decades? Finally, what are some of the societal issues that need to be addressed for the use of AI?

---

Biography: Kewei Sha is an Associate Professor of Data Science within the College of Information at the University of North Texas (UNT). Prior to joining UNT, he held the position of Associate Professor with an approved promotion to Full Professor of Computer Science at the University of Houston-Clear Lake (UHCL). Before that, Dr. Sha has served as Department Chair and Associate Professor in the Department of Software Engineering at Oklahoma City University (OCU). His research interests include Data Management and Analytics, Security and Privacy, Edge Computing, Blockchain, and Internet of Things. As a PI or co-PI, he has successfully secured over 5 million dollars in research funding from NSF, NASA, UNT, UHCL, and OCU. Dr. Sha has published more than 70 publications in prestigious peer-reviewed journals and conferences. Dr. Sha has served as Associate Editors for renowned journals like IEEE IoT Journal (IEEE), Smart Health (Elsevier), Computing (Springer), and Multimedia Tools and Applications (Springer). He has also acted as General Chair and Technical Program Committee (TPC) Chair at many prominent conferences such as ACM/IEEE SEC, ACM/IEEE CHASE, IEEE MOST, IEEE ICCCN, etc. Dr. Sha is a recipient of UHCL President’s Outstanding Research Award, UHCL University Faculty Fellowship Award, IEEE Outstanding Leadership Award, and Anthony and Barbara Lekkos Endowment Faculty Fellowship. He is a senior member of both ACM and IEEE.

Title: Towards Efficient, Secure, and Quality-aware Data Analytics and Sharing
Abstract: Modern applications such as autonomous driving, Internet of Things, online retailing, and social networks, generate extremely large amounts of data. Powerful machine learning and deep learning algorithms are proposed to make intelligent data-driven decisions that recognize the patterns in the data to optimize application performance and increase productivity. However, there are still significant challenges, such as achievements of real-time and reliable data analytics with limited resources, tradeoffs between computation and communication in data analytics, and security in multi-party data sharing. To tackle the above challenges, we have explored techniques such as lightweight algorithms to accurately identify users in real time based on a small set of user activity data, edge computing based frameworks to greatly improve the efficiency of data analytics, and decentralized general security frameworks based on emerging blockchain technologies to enhance secure consortium collaboration.

---