The Australasian Data Science and Machine Learning Conference (AusDM), formerly known as the Australasian Data Mining Conference, has established itself as the premier Australasian meeting for both practitioners and researchers in the fields of Data Science including but not limited to Data Analytics and Data Mining theory and applications and Machine Learning including but not limited Deep Learning and Generative AI. Despite the evolution of these interdisciplinary fields, AusDM is devoted to the art and science of intelligent learning and analysis of (usually big) data sets for meaningful (and previously unknown) insights that could be actionable. Hence, it enables the sharing and learning of research and progress in the local context and breakthroughs in these interdisciplinary fields.

Since AusDM’02, the conference has been providing a forum for disseminating, presenting, showcasing and discussing the latest research and developments in all aspects of Data Science and Machine Learning, including algorithms, software, systems and applications across all industries. Built on this tradition, AusDM’24 will facilitate the cross-disciplinary exchange of ideas, experiences, practices and potential research directions. Specifically, the conference seeks to showcase: Research Prototypes, Industry Case Studies, Practical Technology, and Research Student Projects.

It is aimed to be a meeting place for pushing forward the frontiers of Data Science and Machine Learning in academia and industry. Thus, AusDM’24 will have a variety of engaging activities such as keynote speeches, panel discussions, full paper presentations, tutorials/ workshops, doctoral consortium, social/ networking events, etc.

Submission Guidelines

AusDM’24 proceedings will be published by Springer Communications in Computer and Information Science (CCIS) and made available in due course. The previous AusDM proceedings on SpringerLink is available here.

We invite three types of submissions for AusDM’24:

• Research Track: Academic submissions reporting on new algorithms, novel approaches and research progress, with a paper length of between 8 and 15 pages in Springer CCIS style, as detailed on conference website
• Application Track: Submissions reporting on authentic and practical applications, implementations and experiences or practices. Submissions in this category can be between 6 and 15 pages in Springer CCIS style, as detailed on conference website.
• Industry Showcase Track: Submissions from governments and industry sectors on solutions that have raised profits, reduced costs and/or transformed policy and/or business outcomes/ models can be made in this track. Submissions to this category should be a 1-page extended abstract. Please note that this track is presentation only, without publication in conference proceedings. For publication of your papers, please submit them to the above Application Track.

All submissions, except for the Industry Showcase Track, will go through a double-blind review process, i.e. paper submissions must NOT include authors names or affiliations or acknowledgments referring to funding bodies. Self-citing references should also be removed from the submitted papers for the double-blinded reviewing purpose. The information can be added in the accepted final camera-ready submissions.

AusDM24 values and promotes Diversity, Equity and Inclusion (DEI) in the community and profession. We would like to remind authors to be mindful of not using language or examples that further the marginalisation, stereotyping, or removing of any groups of individuals, especially the marginalised and/or under-represented groups in computing during paper writing.

If Generative AI including Large Language Models (LLMs) is used to assist in preparing the paper submissions, authors are expected to fully describe how Generative AI is used and ensure that all content (e.g. text and figures) of the paper are correct and original. For example, authors should provide how the Generative AI tools were used for paper writing, data processing/ filtering, visualisation, facilitating or running of experiments and proving theorems.

List of Topics

We are calling for papers, both research and applications, and from both academia and industry, for publication and presentation at the conference. All papers will go through double-blind peer review by a panel of international experts.

Please note that AusDM’24 requires that at least one author for each accepted paper register for the conference and present their work for the paper to be published in the proceeding.

AusDM’24 invites contributions addressing current research in Data Science and Machine Learning as well as the experiences, practices, novel applications and future challenges. Topics of interest include, but are not restricted to:

• Data analytics and machine learning over heterogenous data sources including structured, semi-structured, unstructured such as text, graph, sequential, temporal, spatial, spatial-temporal, network, real-time, streaming, web, social media, multimedia, IoT, etc.
• Big data mining and analytics, parallel and distributed data mining and analytics, data stream mining and analytics.
• Computational aspects of data mining and data management.
• Privacy-preserving data mining and analytics.
• Data pre-processing, integration, matching, and linkage.
• Visual analytics and interactive data exploration.
• Machine learning, deep learning, representation learning, reinforcement learning, federated learning.
• Few-shot learning, transfer learning, meta learning, continual learning, multitask learning, multimodal learning.
• Zero-short learning, generative modelling, Large Language Models (LLMs), Large Multimodal Models (LMMs).
• Causal and explainable machine learning.
• Ethical and responsible AI.
• Applications of data science and machine learning in various disciplines such as business, social sciences, education, urban planning, engineering, biomedical and health, sports, humanities, arts, cybersecurity, security and surveillance, environmental science, astronomy, etc.

Contact

Email: ausdm2024@googlegroups.com