Title: The Future of Computing: Integrating AI, Quantum, and Cloud Technologies

Dr. Ahmed Chiheb Ammari

Associate Professor in the Department of Electrical and Computer Engineering

Sultan Qaboos University

Abstract:

In an era defined by rapid technological advancements, the convergence of Artificial Intelligence, Quantum Computing, and Cloud Computing is transforming the very foundations of computing. This keynote will explore how these three transformative technologies are evolving individually and as a cohesive force, creating unprecedented opportunities for innovation across industries, scientific research, and society at large.

Artificial Intelligence enables intelligent decision-making and automation, reshaping sectors from healthcare to finance. Quantum Computing, with its potential to solve complex problems beyond the reach of classical computers, promises breakthroughs in fields like cryptography, materials science, and drug discovery. Meanwhile, Cloud Computing democratizes access to these powerful technologies, providing a scalable, collaborative infrastructure that accelerates development and deployment.

As these technologies converge, they unlock possibilities that were once only imagined. This keynote will delve into the synergies created by this integration, offering insights into how organizations can harness these advances to solve critical challenges, drive efficiencies, and build a sustainable, technology-driven future. Attendees will gain a visionary perspective on the future of computing, understanding both the immense potential and the practical considerations of integrating AI, Quantum, and Cloud technologies to shape a new era of innovation.

Bio:

Dr. Ahmed Chiheb Ammari is an Associate Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Sultan Qaboos University, bringing over 25 years of experience in academia and research. He earned his Ph.D. in Electrical Engineering from the Institut National Polytechnique de Grenoble (INPG) in France and has held visiting scholar positions at renowned institutions, including the University of Southern California and North Carolina State University.

Dr. Ammari’s research covers a diverse array of advanced topics, including artificial intelligence systems, energy-efficient computing, embedded computer vision, and inductive power transfer for implantable medical devices. He has led significant research initiatives focused on energy-efficient data center operations and the integration of renewable energy technologies. His scholarly work includes numerous publications in high-impact international journals and two U.S. patents in energy-efficient task scheduling.

A Senior Member of IEEE since 2015, Dr. Ammari is an active contributor to the professional community, serving on academic committees, participating in conferences, and working as an associate editor for international journals.

Title: Human-AI collaboration in Education: the Hybrid Future

Dr. Inge Molenaar

Director of the National Education Lab AI (NOLAI) and a Professor of Education and Artificial Intelligence at the Behavioural Science Institute

Radboud University, Netherlands

Abstract:

In her keynote address, Inge Molenaar will explore the role of artificial intelligence in education, emphasizing the potential for hybrid human-AI collaboration. At the heart of effective education is the goal of fostering student learning and talent development. To achieve this, it is essential to align theories and scientific insights on learning and teaching with the possibilities offered by AI.

Molenaar will highlight the dual role of AI in education, as a tool and an actor. AI as a tool is used for understanding learners’ learning processes and refining learning theories. AI, as an actor, supports learners during learning and helps teachers improve their teaching. A significant gap exists in the application of theories and scientific insights in AI-empowered educational technologies. Only 50% of learner-facing solutions and 33% of teacher-facing solutions are grounded in these theories, indicating substantial room for improvement.

In her talk, Molenaar will outline how to design AI in education to connect learning theories and scientific insights with the possibilities of AI. She will contrast the replacement and augmentation perspectives and address the consequences of AI automation on teacher and learner autonomy. Through compelling examples, she will illustrate how AI in education can lead to the replacement, complementation, and augmentation of teachers and learners.

Moreover, she will propose an innovative approach to investigate the complex interplay among AI, teachers, and students, providing a practical framework for integrating AI’s dual role with the established scientific understanding of learning and teaching. This alignment may help to cultivate responsible educational practices that empower students to realize their full potential.

Bio:

Inge Molenaar is the Director of the National Education Lab AI (NOLAI) and a Professor of Education and Artificial Intelligence at the Behavioural Science Institute, Radboud University, Netherlands. With over 20 years of experience in technology-enhanced learning, she has successfully navigated roles ranging from entrepreneur to academic.

Dr. Molenaar's research focuses on innovative, technology-empowered approaches to optimize human learning and teaching. Central to her work is the application of data, learning analytics, and artificial intelligence to understand the dynamics of learning over time.

Envisioning Hybrid Human-AI Learning Technologies, Dr. Molenaar aims to augment human intelligence with artificial intelligence, empowering both learners and educators to make education more efficient, effective, and responsive to individual needs. Her research group, the Adaptive Learning Lab, investigates how self-regulated learning can be supported through technology. At the National Education Lab AI, she develops new educational practices using AI and explores the responsible use of AI in education.

She is a recipient of several prestigious grants, including an ERC Starting Grant and funding for the National AI and Education Lab (NOLAI) and holds Master's degrees in Cognitive Psychology and International Business Studies, as well as a PhD in Educational Sciences from the University of Amsterdam.

Development and application of biostatistical methods to improve predictive modeling of clinical data for precision medicine

Dr. Muthuraman Muthuraman

Associate Professor
Chair of the Informatics for Medical Technology
University Augsburg

APL-Professor
Chair of the Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI)
University clinic Würzburg
Germany

Abstract:

The primary aim of this talk to develop and apply biostatistical methods to improve predictive modeling of data for precision medicine. In order to achieve such a solid framework for different type of datasets the selection of methods are important. However, currently their is no universal method which can be applied for data gathered from humans.

To understand the given data at hand, first statistical tools which considers the distribution of the data namely Bayesian posterior distribution will be discussed. Second, to counter one of the most important problems in most of the clinical datasets is the demographics namely matching cohorts for sex would be considered with propensity score matching analyses. In order to model complex datasets, with several input and output variables the structural equation modelling will be delibrated. After understanding and modelling the datasets the prediction will be covered with some machine and deep learning approaches and finally some applications to datasets from multimodal, longitudinal and signal based analyses will be explored.

For each methodological aspect an example will be provided with obtained results. The applications will be highlighted with examples and corresponding results.

Taken together, the integration of methods leading to the individualized prediction of each subject will be demonstrated.

Bio:

Muthuraman Muthuraman was born in Chennai, India, in 1980. He received the B.E. degree in electronics and communication engineering from the University of Madras, Madras, India, in 2002, and the MS in digital communications in Christian Albrecht’s University, Kiel, Germany in 2006. Ph.D. degree in biomedical engineering from the technical faculty and Department of Neurology of Christian Albrecht’s University, Kiel, Germany, in 2010. In 2010, he joined the Department of Neurology, University of Kiel, as a Post-doc, and in 2013 became a senior post-doc. Since December 2016, he has been with the Department of Neurology, Johannes Gutenberg University Mainz, where he is an Assistant Professor, and the head of the department biomedical statistics and multimodal signal processing unit. Currently from 2024 he is heading the group Informatics for Medical Technology (IMT) in Augsburg as an associate professor and second affiliation to Julius Maximilian university of Würzburg in the department of Neurology and head of the group Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI). His current research interests include mathematical methods for time series analysis and source analysis on oscillatory signals, sleep, function of oscillatory activity in central motor systems, biomedical statistics, connectivity analyses, multimodal signal processing and analyses of EEG, MEG, fMRI and EMG, structural and network analyses on anatomical MRI and DTI, functional network analyses on PET imaging, machine learning and deep learning, network analyses on proteomic and genomic data, RNA, mRNA and Spatial transcriptomics.

Title: Concepts of Granular Computing: An Introduction

Dr. Rami Al-Hmouz

Associate Professor in the Department of Electrical and Computer Engineering

Sultan Qaboos University

Abstract:

Granular Computing (GrC) is a pioneering computational paradigm designed to process complex information systems by leveraging the concept of information granules—cohesive clusters of related elements grouped by similarity, functionality, or proximity. This seminar introduces the foundational concepts of information granules and explores their role in handling uncertainty, imprecision, and high-dimensional data. It highlights Granular Computing and its agenda, focusing on simplifying computation, enhancing interpretability, and enabling efficient decision-making. The discussion covers key formal models of information granules, including Interval Analysis for approximating data ranges, Fuzzy Sets for representing uncertainty through degrees of membership, Rough Sets for addressing incomplete or inconsistent information, and Shadowed Sets for dividing data into clear, uncertain, and shadowed regions. By examining these models, participants will gain a comprehensive understanding of GrC's theoretical foundations and practical applications in fields like data mining, machine learning, and decision support systems, equipping them with tools to address complex computational challenges effectively.

Bio:

Rami Al-Hmouz (Senior Member, IEEE) received his Ph.D. in Computer Engineering from the University of Technology Sydney, Australia, in 2008. He is currently a faculty member in the Department of Electrical and Computer Engineering at Sultan Qaboos University. Previously, he served as a professor at King Abdulaziz University, Saudi Arabia. His research interests include granular computing, fuzzy modeling, and machine learning approaches. He has made significant contributions to these fields, with an extensive publication record in prestigious refereed journals and conferences. His scholarly work has been supported by substantial research grants and has received numerous awards. Additionally, he has served on technical program committees for several international conferences and is currently an associate editor for Information Sciences and the International Journal of Fuzzy Systems.

Title: Enhancing Earthquake Forecast with Artificial Intelligence and Machine Learning: A Technical Perspective

Dr. Jihad Qaddour

Professor at Illinois State University

Abstract:

Earthquake forecasting remains a significant challenge in geoscience due to seismic activity's complex and nonlinear nature. Traditional methods, which rely on geological observations and statistical analyses, often struggle to predict the timing and location of seismic events accurately. Recent advancements in artificial intelligence (AI) and machine learning (ML) present a promising alternative by leveraging their ability to identify complex patterns and integrate diverse datasets. This paper examines the application of AI/ML techniques in earthquake forecasting, highlighting the effectiveness of neural networks, deep learning models, and support vector machines (SVM). Neural networks excel at detecting nonlinear relationships in seismic data, while deep learning models, such as convolutional and recurrent neural networks, analyze spatial and temporal patterns to identify anomalies. SVMs contribute by classifying seismic events and distinguishing between normal and anomalous activity, enhancing early warning systems. By addressing the limitations of traditional approaches, AI/ML techniques offer a transformative pathway to improve earthquake forecasting accuracy, timeliness, and reliability.

Bio:

Dr. Jihad Qaddour earned his Ph.D. in Electrical Engineering in 1990, an M.S. in Electrical Engineering in 1987, and an M.S. in Mathematics in 1992 from Wichita State University. Before that, he obtained a B.S. in Electrical Engineering from Damascus University. He is a tenured professor at Illinois State University (ISU), teaching graduate and undergraduate networking and telecommunications courses since 2002. He has authored or co-authored over 70 peer-reviewed research papers and more than 30 industrial technical reports. Dr. Qaddour has taught a wide range of courses in electrical engineering, including telecommunications, networking, signal processing, digital electronics, wireless and mobile security, AI and deep learning, and mathematics. He has served on over 40 academic and research committees and edited over 30 articles for journals and international conferences. Additionally, he has participated in program committees and served as a session chair at several international conferences. Dr. Qaddour was a grant reviewer for the Broadband Technology Opportunities Program (BTOP), which involved reviewing a $7.4 billion stimulus package to expand broadband access to rural and underserved areas—contributing significantly to President Obama's initiative to enhance nationwide broadband access. He received training to become a certified grant reviewer, and through this effort, President Obama awarded six projects totaling $45,927,204, which Dr. Qaddour reviewed. Before joining ISU, Dr. Qaddour worked at Sprint PCS and Sprint Broadband from 1998 to 2002, where he held various roles in research, analysis, design, planning, and development of new technology systems. He gained expertise in wireless mobile networks and communication systems during his industrial career. Prior to Sprint, he was a tenured associate professor at Mesa State College until 1998, teaching engineering and mathematics courses. He also founded and served as CEO of Global Advanced Telecommunications in Bahrain. Dr. Qaddour believes that academia has a responsibility to impact communities positively. He founded and chaired the board of Syria Relief & Development (SRD Foundation) in 2011, which has provided over $135 million in aid to more than 23 million beneficiaries in Syria. In 2017, he also founded and chaired the board of the International University of Science and Renaissance (IUSR), which currently has close to 5000 students and 26 programs, using a hybrid learning model to adapt to challenging environments. He is deeply committed to engaging with communities worldwide to improve the lives of underserved students and communities.

Title: Fuzzy Sets and Artificial Intelligence under Innovation

Dr. Muhammet Deveci

Professor

Department of Industrial Engineering

Turkish Naval Academy, National Defence University

Istanbul, Turkey

Abstract:

Fuzzy sets, one of the Artificial Intelligence (AI) tools, are widely used in industrial applications such as control systems engineering, image processing, power engineering, industrial automation, robotics, consumer electronics, language processing and optimization. It is extensively used in modern control systems such as in air conditioners, automobile and vehicle subsystems as automatic transmissions, ABS and cruise control, cameras, elevators, language filters on message boards and chat rooms for filtering out offensive text, animation-based films, pattern recognition in remote sensing, video game artificial intelligence, dishwashers, and washing machine. These are some of the common applications of the Fuzzy Logic. Artificial intelligence theory and applications are based on fuzzy set theory such as fuzzy machine learning, fuzzy deep learning, fuzzy data mining, fuzzy big data analysis, and swarm intelligence. Metaheuristics such as ant colony optimization, artificial bee colony optimization, particle swarm optimization, tabu search, genetic algorithms, and simulated annealing is other modeling techniques based on AI. This talk will discuss the relationship between fuzzy sets and AI and their applications.

Bio:

Dr. Muhammet Deveci is a Full Professor at the Department of Industrial Engineering in the Turkish Naval Academy, National Defence University, Istanbul, Turkey, and he is Honorary Senior Research Fellow with the Bartlett School of Sustainable Construction, University College London, UK. Dr. Deveci is also a Visiting Professor at Royal School of Mines in the Imperial College London, London, UK. He worked as a Visiting Researcher and Postdoctoral Researcher, in 2014-2015 and 2018–2019, respectively, with the School of Computer Science, University of Nottingham, Nottingham, U.K. Dr Deveci is an outstanding researcher and a prolific author who has been publishing high quality peer-reviewed papers in highly ISI ranked journals and reputable international conferences. Dr. Deveci has published over 340 papers in journals indexed by SCI/SCI-E papers at reputable venues, as well as more than 30 contributions in International Conferences related to his areas. Dr. Deveci received the 100th-anniversary award for his worldwide scientific achievements from the Scientific and Technological Research Council of Turkey (TUBITAK).

Dr. Deveci has also been engaged with the wider community providing academic service through chairing/organising conferences, streams, tutorials, reviewing papers, and acting as Editorial Board Member of well-known journals including IEEE Transactions on Fuzzy Sets (IEEE TFS), IEEE Transactions on Intelligent Vehicles (T-IV), IEEE Transactions on Emerging Topics in Computational Intelligence, Information Sciences, Applied Soft Computing, Engineering Applications of Artificial Intelligence, Artificial Intelligence Review, and more. Additionally, he has strong international links with colleagues carrying out research in the field of his expertise. And he has worked as a guest editor for many international journals such as IEEE Transactions on Fuzzy Systems (TFS), Applied Soft Computing, Annals of Operations Research, Sustainable Energy Technologies and Assessments, Journal of Petroleum Science and Engineering, and International of Journal of Hydrogen Energy (IJHE).

Dr Deveci is an internationally recognized outstanding scientist in intelligent decision support systems underpinned by computational intelligence, particularly uncertainty handling, fuzzy systems, combinatorial optimization, and multicriteria decision making. His research and development activities are multidisciplinary and lie at the interface of Operational Research, Computer Science and Artificial Intelligence Science. Based on the 2020, 2021, 2022 and 2023 publications from Scopus and Stanford University, he is within the world's top 2% scientists in the field of Artificial Intelligence. He has been tackling challenging real-world problems without stripping off their complexities, which include climate change, renewable energy, sustainable transport, and urban mobility.