AI-driven Data Science: Emerging Theories and Cross-Disciplinary Frontiers
This special session is organised and supported by the following educational partners
Prince Sultan University and Taibah University, Saudi Arabia
Session Chair:
- Prof. Tanzila Saba, Prince Sultan University, Riyadh, Saudi Arabia
Dr. Liyakatunisa Syed, Taibah University, Madinah, Saudi Arabia
Session Co-Chairs:
Dr Amjad Rehman, Prince Sultan University, Riyadh, Saudi Arabia
Dr Hoshang Kolivand, Liverpool John Moores University, Liverpool, United Kingdom
Synopsis:
AI-driven Data Science is redefining how knowledge is extracted, interpreted, and applied across disciplines. Combining powerful algorithmic advances with scalable data infrastructures, this evolving field integrates artificial intelligence, machine learning, statistical modeling, and domain expertise to solve complex real-world problems. Recent breakthroughs in generative AI, explainable models, foundation models, and big data ecosystems are expanding the capabilities of Data Science in areas ranging from healthcare and robotics to finance, education, the digital humanities, and environmental sustainability. These innovations are not only transforming scientific workflows but also reshaping business intelligence, policymaking, and everyday decision-making in smart systems and connected environments.This special session aims to highlight both the theoretical developments and practical applications of AI-infused Data Science. It welcomes contributions that explore innovative methodologies, interdisciplinary collaborations, and the societal impact of AI-powered analytics. By bringing together researchers and practitioners from a wide array of domains, the session provides a dynamic platform for discussing challenges, opportunities, and the future trajectory of intelligent data science.
Topics:
Topics of interest include, but are not limited to:
- Emerging Theories and Foundations of AI-Driven Data Science
- Machine Learning, Deep Learning, and Foundation Models
- Explainable and Trustworthy AI in Data Science
- Generative AI and Synthetic Data Applications
- Data Science for Health, Sustainability, and Smart Cities
- Multimodal Learning and Fusion of Heterogeneous Data
- AI for Social Good and Ethical Data Practices
- Data-Driven Research in Digital Humanities and Social Sciences
- Real-Time and Stream Data Analytics
- Edge AI and Intelligent IoT Systems
- Biomedical Informatics and Personalized Medicine
- Learning Analytics and Smart Education Environments
- AI in Business Intelligence and Market Prediction
- Cybersecurity, Privacy, and Data Protection
- Crowd Analytics and Urban Mobility
- Data Visualization and Interpretability
- Interdisciplinary Applications in Law, Finance, and the Arts
Paper Submission
Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system