Conference Description
Key Takeaways
- Hybrid academic conference spanning machine learning, computing, data science, and artificial intelligence
- Dual venue format with symposiums at the University of Strathclyde and Illinois Institute of Technology
- Peer-reviewed paper submissions with publication opportunities in conference proceedings and partner journals
- Research applications spanning robotics, financial risk prediction, and scientific discovery
- Designed for researchers, academics, students, and industry practitioners working in data-driven technologies
Introduction
The 8th International Conference on Computing and Data Science (CONF-CDS 2026) convenes researchers, academics, and industry professionals working at the intersection of computing technology, machine learning, and data science. As organisations across sectors grapple with increasingly complex data environments and the rapid maturation of artificial intelligence capabilities, forums that bridge theoretical research with practical implementation have become essential for advancing the discipline.
This hybrid conference offers both online participation and in-person symposiums, reflecting the global nature of contemporary computing research and the need for accessible knowledge exchange across geographic boundaries.
About CONF-CDS 2026
Now in its eighth year, CONF-CDS has established itself as a platform for disseminating peer-reviewed research in computing and data science. The conference operates across two institutional venues: the University of Strathclyde in the United Kingdom and the Illinois Institute of Technology in the United States. This transatlantic arrangement enables broader participation while maintaining the academic rigour associated with established research institutions.
The event follows a traditional academic conference structure, incorporating keynote presentations, paper sessions, and poster displays. All submitted papers undergo peer review, with accepted works published in conference proceedings. Select papers may also appear in Micromachines, an MDPI partner journal, providing authors with additional publication pathways.
Research Themes and Technical Focus Areas
The conference programme encompasses several interconnected domains within computing and data science. Machine learning research features prominently, with coverage extending from deep learning architectures to reinforcement learning methodologies and computational optimisation techniques. These areas continue to drive advances in pattern recognition, decision-making systems, and automated reasoning.
Core computing topics include computer modelling and simulation alongside cloud computing infrastructure. These foundational technologies underpin much of the applied research presented at the conference, providing the computational substrate upon which machine learning and data analytics systems operate.
Data science sessions address data mining, big data processing, and analytics methodologies. As data volumes continue to expand across industries, techniques for extracting meaningful insights from large-scale datasets remain central to both academic inquiry and commercial application.
Research presented at previous editions has explored technologies including edge computing devices, large language models, CNN-Transformer hybrid architectures, natural language processing, knowledge graphs, distributed storage systems, high-definition mapping, and Lidar sensing. These topics reflect the breadth of current investigation in the field, spanning from fundamental algorithmic research to sensor-based applications in autonomous systems.
Applied Research and Industry Applications
Beyond foundational research, CONF-CDS 2026 examines practical applications of computing and data science across multiple domains. Robotics research explores how machine learning enables more sophisticated autonomous behaviour, while financial risk prediction work addresses the growing role of algorithmic systems in economic decision-making. Programme search and scientific discovery applications demonstrate how computational methods are accelerating research processes across disciplines.
This emphasis on applied research reflects the maturing relationship between academic computing research and industrial implementation. As artificial intelligence systems move from laboratory environments into production deployments, conferences that facilitate dialogue between researchers and practitioners serve an increasingly important function.
Who Should Attend
CONF-CDS 2026 serves several distinct constituencies within the computing and data science community. Academic researchers, including professors, postdoctoral fellows, and doctoral candidates, will find opportunities to present work, receive peer feedback, and identify potential collaborators. Graduate students benefit from exposure to current research directions and networking with established scholars in their fields.
Industry professionals working in technology, artificial intelligence, and data-driven sectors can engage with emerging research that may inform future product development or operational improvements. The conference also attracts representatives from research institutes and technology organisations seeking to maintain awareness of academic advances relevant to their work.
The Value of Cross-Disciplinary Exchange
Computing and data science research increasingly requires collaboration across traditional disciplinary boundaries. Machine learning advances depend on mathematical optimisation theory, while practical data science applications demand domain expertise in fields ranging from healthcare to finance. CONF-CDS 2026 provides a venue where these diverse perspectives can converge, potentially catalysing research directions that might not emerge within more narrowly focused gatherings.