Conference Information

AusDM 2026: Australasian Data Science and Machine Learning Conference

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Submission Date:
2026-07-05 Due in 9 days
Notification Date:
2026-08-10
Conference Date:
2026-12-02
Location:
Sydney, Australia
Years:
24
ICORE: C   Viewed: 19675   Tracked: 2   Attend: 0

Call For Papers

AusDM 2026 (Australasian Data Science and Machine Learning Conference) is a ICORE C conference held in Sydney, Australia on 2026-12-02. The paper submission deadline is 2026-07-05. Acceptance notifications are sent on 2026-08-10.

The Australasian Data Science and Machine Learning Conference (AusDM), formerly known as the Australasian Data Mining Conference, has become the leading regional forum for both researchers and practitioners in Data Science and Machine Learning, including areas such as Data Mining, Data Analytics, Deep Learning, Natural Language Processing, and Generative AI. As these interdisciplinary fields continue to evolve, AusDM remains committed to advancing the development and application of intelligent algorithms capable of learning from large-scale, complex data. The conference plays a vital role in sharing progress and fostering collaboration within the Australasian data science community, while also showcasing breakthroughs with global relevance. Since its inception in 2002, AusDM has provided a platform for the presentation, discussion, and dissemination of advances in algorithms, systems, software, and real-world applications. AusDM'26 will build on this tradition by encouraging cross-disciplinary exchange of ideas, practical experiences, and future research directions. AusDM'26 aims to be a dynamic meeting point for advancing the frontiers of Data Science and Machine Learning in both academia and industry. The program will feature keynote talks, panel sessions, paper presentations, workshops and tutorials, a doctoral consortium, and networking events, ensuring a rich and engaging experience for all participants. Topics of Interest We seek contributions in, but not limited to, the following areas: Foundational Techniques in Machine Learning and AI Supervised, unsupervised, semi-supervised and self-supervised learning. Deep learning and representation learning. Reinforcement learning and federated learning. Transfer learning, meta learning, few-shot and continual learning. Multitask and multimodal learning. Generative models, including GANs and diffusion models. Large Language Models (LLMs) and Large Multimodal Models (LMMs). Zero-shot and prompt-based learning. Learning from Diverse and Complex Data Analytics over structured, semi-structured, and unstructured data. Text, time-series, graph, spatial, spatio-temporal, and network data. Web, social media, multimedia, IoT, and sensor data. Sequential, temporal, and dynamic data modelling. Data-Centric AI and Data Engineering Data preprocessing, cleaning, integration, matching, and linkage. Privacy-preserving and secure data mining. Data-centric AI pipelines and dataset curation. Computational aspects of data mining and large-scale data management. Scalable and Real-Time Data Analytics Big data analytics and scalable ML. Parallel and distributed learning algorithms. Data stream mining and real-time analytics. Edge, cloud, and IoT-enabled ML systems. Interactive and Visual Analytics Visual analytics and explainability through visualisation. Human-in-the-loop machine learning. Interactive data exploration and decision support. Responsible, Causal, and Explainable AI Explainable and interpretable machine learning. Fairness, accountability, transparency, and ethics in AI. Causal inference and causal machine learning. Robustness, generalization, and uncertainty quantification. Applied Data Science and ML Across Domains Applications in business, finance, education, agriculture, urban planning, healthcare, sports, social sciences, cybersecurity, arts, and humanities. Domain-specific AI systems in biomedical informatics, environmental science, astronomy, engineering, and more. Industrial case studies and data-driven product innovations.
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