AusDM'24: 22nd Australasian Data Science and Machine Learning Conference Call for Papers

Conference Details

• Conference Name: Australasian Data Science and Machine Learning Conference (AusDM'24)
• Formerly: Australasian Data Mining Conference
• Location: RMIT University, Melbourne, Australia
• Dates: November 27-29, 2024 (Note: Main website indicates Nov 25-27, 2024)
• Website: https://ausdm24.ausdm.org/
• Submission System: https://easychair.org/conferences/?conf=ausdm24

Overview

The Australasian Data Science and Machine Learning Conference (AusDM) is the premier Australasian meeting for practitioners and researchers in Data Science (including Data Analytics, Data Mining, Deep Learning, and Generative AI) and Machine Learning. It is devoted to the art and science of intelligent learning and analysis of data sets for meaningful insights. AusDM'24 aims to facilitate the cross-disciplinary exchange of ideas, experiences, practices, and potential research directions, showcasing Research Prototypes, Industry Case Studies, Practical Technology, and Research Student Projects.

The theme for AusDM'24 is "Entering a new world driven by Data Science and Machine Learning."

Diversity, Equity and Inclusion (DEI) Statement

AusDM’24 promotes an inclusive environment and encourages the open expression and exchange of ideas, free from all forms of discrimination, retaliation, and harassment. AusDM’24 is committed to empowering diverse, equitable, and inclusive participation.

Submission Guidelines

AusDM’24 proceedings will be published by Springer Communications in Computer and Information Science (CCIS).

We invite three types of submissions:

1. Research Track:

   • Academic submissions reporting on new algorithms, novel approaches, and research progress.
   • Paper length: 8 to 15 pages in Springer CCIS style.
   • Double-blind review process: Paper submissions must NOT include authors' names, affiliations, or acknowledgments. Self-citing references should also be removed.

2. Application Track:

   • Submissions reporting on authentic and practical applications, implementations, and experiences or practices.
   • Paper length: 6 to 15 pages in Springer CCIS style.
   • Double-blind review process: Paper submissions must NOT include authors' names, affiliations, or acknowledgments. Self-citing references should also be removed.

3. Industry Showcase Track:

   • Submissions from governments and industry sectors on solutions that have raised profits, reduced costs, and/or transformed policy or business outcomes/models.
   • Submission format: 1-page extended abstract.
   • Presentation only: This track is for presentation only, without publication in conference proceedings. For publication, submit to the Application Track.

Important Notes on Submissions:

• Generative AI Usage: If Generative AI (including LLMs) is used in paper preparation, authors must fully describe its use and ensure all content is correct and original.
• DEI Awareness: Authors should be mindful of not using language or examples that marginalize, stereotype, or remove any groups of individuals, especially marginalized and/or under-represented groups in computing.
• Presentation Requirement: At least one author for each accepted paper must register for the conference and present their work for the paper to be published in the proceedings.

List of Topics

Topics of interest include, but are not restricted to:

• Data analytics and machine learning over heterogeneous data sources (structured, semi-structured, unstructured, text, graph, sequential, temporal, spatial, 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-shot learning, generative modeling, 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 (business, social sciences, education, urban planning, engineering, biomedical and health, sports, humanities, arts, cybersecurity, security and surveillance, environmental science, astronomy, etc.).

Important Dates (AoE, 11:59 PM)

• Abstract Submission: Sunday, August 25, 2024
• Paper Submission: Sunday, September 1, 2024
• Paper Notification: Sunday, September 22, 2024
• Camera-Ready Submission: Monday, September 30, 2024
• Author Registration: Monday, September 30, 2024
• Conference Dates: Monday, November 25 to Wednesday, November 27, 2024

Sponsors

• RMIT EAIDA Hub
• Amazon Web Services

Contact

• Email: ausdm2024@googlegroups.com