Advances and Challenges of Applying AI/GenAI in Design and Process Science : The 2024 International Conference of the Society for Design and Process Science (SDPS)

This book presents the proceedings of the 25th Society for Design and Process Science Conference (SDPS 2024), which took place in Bologna, Italy on October 6-9, 2024. The 2024 conference centers on generative artificial intelligence (GenAI) and its impact on academia and industry. The papers focus on the generational challenges that GenAI presents, which industry leaders agree constitutes a sea change in technology not seen since the Industrial Revolution. The conference emphases that, as the AI/GenAI community creates vast new and novel opportunities at a rapid pace, it consequently creates challenges with lack of understanding and immature designs, processes, research, engineering, while also exposing gaps in formal AI/GenAI design and process education. The authors also discuss, for the sake of analysis, the general fear of unknown AI/GenAI technologies, as well as system ambiguity and disguised complexities. The authors aim to present solutions that help to mitigate risk in these in critical environments (e.g., medical, financial, cyber defense etc). To better serve developers and modern business leaders with guidance to innovate, mitigate risk, and capitalize on novel advancements in AI/GenAI, the Society for Design and Process Science (SDPS) brings together the advanced academic research community with industry to more qualitatively harness the power of formal AI/GenAI design and process to accelerate science and business.

Elisabetta Ronchieri is a computer scientist working as a researcher at the Italian National Institute for Nuclear Physics (INFN). She is currently a member of the Distributed System (DS) group in the Software Development and Distributed Systems (SDDS) department at INFN CNAF, the INFN National Center dedicated to research and development on IT technologies, located in Bologna, Italy. She is constantly collaborating with students, colleagues and international collaborators (her publications are available on request). In the latest years, Dr. Ronchieri has started collaborating with the University of Bologna as Adjunct Professor for Machine Learning courses.  Dr Ronchieri held a PhD in Automation, Bioengineering and Robotics from the University of Pisa (2007), Msc in Computer Engineering from the University of Pisa. Her research topics are related to anomaly detection and text analysis, exploring new AI solutions.  

John Carbone has served the defense industry as an Engineering Fellow, Chief Science Advisor, Technology Director, Chief Engineer for Innovation, Chief Data Scientist, and Applied AI with AI Ethics and AI Security, as well as, Advanced Data Science as Adjunct Professor at Southern Methodist and Baylor University for over 35 years. His national and international innovations/patents were instrumental in forging bridges between Cloud and Big Data/Cloud warfighting architectures, C5ISR enterprise, MDA Non-Kinetic ISR Algorithms, MESH comms architectures, UAV Sensor Fusion, JADC2 dominance focused 5G designs, dynamic DDIL communications, AI/behavior-based, and recent Space-based SDWAN & cross domain cyber security. Dr. Carbone currently serves as Senior Technical Director & Chief CIP Solutions Architect @ Forcepoint, LLC, Global Governments and Critical Infrastructure while developing and teaching transformational master’s and PhD curriculum on Applied Artificial Intelligence, Self-Learning Machines, and Applied Data Science at Baylor and Southern Methodist University.  Dr. Carbone has a 100+ AI, Engineering, and Data Science publications incl. AI based Cyber Security, Mining Big Data to Improve National Security, Multi-Disciplinary Systems Engineering, and Applied Cyber Physical Systems to name a few, His newest book by Springer Publishing regarding AI, chatbots, and Large Language Models is “Chatbots, The good, The Bad, and The Ugly”

Patrick Then heads School of Information and Communication Technologies at Swinburne University of Technology Sarawak Campus and program leader of Health Data Analytics at Swinburne Melbourne Data Science Institute. He is the founder and director of Swinburne Sarawak’s Centre for Digital Futures. He is the thought leader in health informatics and digital health research and development. He demonstrated translational research from fundamental research to solving real-life cardiac challenges faced by clinicians and patients. He has been a plenary speaker of various national conferences in digital health. One of his digital health in cough sound artificial intelligence was rolled out to the whole nation in October 2022. The cough sound AI platform is specialised to detect respiratory diseases ranging from covid19 to asthma.

Dr Radmila Juric is an SDPS fellow, with 30+ years of experience as an academic, working in the UK Higher Education, in various capacities: from lecturing, course development and management, and academic quality assurance, to research management, supervision, and editorial jobs.  Dr Juric has published extensively, contributed towards academic events in various roles, delivered talks in academia and in industry and encouraged young scholars to undertake interdisciplinary research, which she has been passionately promoting for decades.  Her research interest is wide and spans decades of publishing on software interoperability, semantic technologies, software architectures, pervasive computational environments, translational bioinformatics and current format of AI.  Over the last five years Dr Radmila Juric has focused on current trends in computing, which range from intelligent computational edge, computing continuum and human machine augmentation to addressing problems of obscurity, explainability and bias of AI algorithms and the way we evaluate AI models.  The synergy of logic and predictive inference, emerging from learning technologies, has triggered her interests in addressing emerging problems in transdiagnostic dimension of mental health disorders and computational psychiatry and modelling the semantic of prompting mechanisms in generative AI.