The 23rd Australasian Data Science and Machine Learning Conference (AusDM'25)

Brisbane, Australia | 26-28 November 2025

Call for Papers

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 2025 will build on this tradition by encouraging cross-disciplinary exchange of ideas, practical experiences, and future research directions.

This year's conference will highlight:

  • Research prototypes
  • Industry case studies
  • Innovative technologies
  • Research student projects

AusDM'25 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

AusDM'25 invites high-quality submissions from both academia and industry that address novel research contributions, practical applications, and emerging challenges in Data Science, Machine Learning, and Artificial Intelligence. We welcome papers presenting original research, real-world case studies, and deployed systems, spanning both theoretical developments and applied innovations. All submissions will undergo a double-blind peer review by an international panel of experts.

Accepted papers will be published in the conference proceedings, and at least one author per accepted paper must register and present at the conference for inclusion in the proceedings. 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.

Submission

The proceedings of AusDM'25 will be published by Springer in the Communications in Computer and Information Science (CCIS) series and will be made available via SpringerLink. Previous AusDM proceedings can be accessed here.

We invite three types of submissions for AusDM'25, from both academia and industry, reporting on novel research, practical implementations, and real-world experiences in Data Science, Machine Learning, and Artificial Intelligence:

  • Research Track: Submissions reporting on novel algorithms, models or theories; foundational or empirical research; and methodological advances. The paper length should be between 8 and 15 pages in Springer CCIS style, as detailed on conference website.
  • Application Track: Submissions reporting on authentic and practical implementations and case studies; industry or government deployments; and lessons learned, impacts, and domain-specific innovations. Submissions in this category can be between 6 and 15 pages in Springer CCIS style, as detailed on conference website.
  • Industry Showcase Track: We also invite submissions from industry and government authors who wish to only showcase their work without publication. 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.

Ethical and Inclusive Research

AusDM'25 upholds the principles of Diversity, Equity, and Inclusion (DEI). We encourage authors to:

  • Avoid language or examples that reinforce marginalization or stereotypes.
  • Be respectful and inclusive in how research is framed and presented.

    • Use of Generative AI (e.g., LLMs)

    If Generative AI tools (such as ChatGPT or others) are used in preparing the submission, authors must:

  • Clearly disclose how these tools were used (e.g., for writing, data processing, visualisation, experimentation, etc.).
  • Ensure the accuracy, originality, and correctness of all content generated. Maintain full responsibility for the integrity of the submission.

    • Journal Special Issue

    A selected number of best papers will be invited for a submission to AusDM Special Issue in a Scimago ranked journal. The information of past Special Issue is available below:

    AusDM24 Special Issue

    AusDM23 Special Issue

    AusDM22 Special Issue

    Important Dates (AoE, 11.59pm)

    • Abstract submission: 10 Aug 25 17 Aug 25
    • Paper submission: 17 Aug 25 24 Aug 25
    • Paper Notification: 7 Sept 25 21 Sept 25
    • Camera-Ready Submission: 21 Sept 25 5 Oct 25
    • Author Registration: 21 Sept 25 5 Oct 25
    • Conference Date: 26--28 Nov 25