The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference with a long-standing tradition in data mining, knowledge discovery, machine learning, and data science. The conference provides an international peer-reviewed forum for presenting original research, exchanging innovative ideas, and discussing emerging challenges and applications.

Hosted in Wellington, New Zealand, from 29 June to 2 July 2027, PAKDD 2027 continues this tradition with an engaging programme of keynote presentations, peer-reviewed research papers, workshops, tutorials, and a doctoral consortium. As a well-regarded conference with a strong research community, PAKDD brings together leading researchers and practitioners through invited talks and high-quality refereed research papers.

We invite high-quality submissions across four tracks, covering theoretical advances, novel methodologies, and practical applications of data science.

---------------------------------------------------------------------------------------
Submission Tracks
---------------------------------------------------------------------------------------
PAKDD 2027 welcomes submissions across four tracks:
1. Research Track: Novel, original, and high-quality research in knowledge discovery, data mining, machine learning, and data science, spanning theoretical foundations, learning methods, big data, trustworthy AI, security and privacy, emerging topics, and interdisciplinary applications.

2. Survey Track: Surveys, systematic reviews, comparative studies, and vision papers that synthesise existing research, develop new taxonomies and frameworks, critically assess methods and benchmarks, and identify emerging directions and open challenges in data science.

3. Special Track: LLM and Agentic AI in Data Science - Innovative research on LLMs and Agentic AI for data science and knowledge discovery, covering data preparation, knowledge extraction, retrieval-augmented generation, autonomous agents, and reliable, secure, and responsible use.

4. Applied Data Science Track: Real-world data science research addressing practical challenges and domain needs, with a focus on sound methodology, realistic evaluation, and impact, including deployed systems, pilots, case studies, and rigorous evaluations.

---------------------------------------------------------------------------------------
Topics of Interest
---------------------------------------------------------------------------------------
We welcome submissions covering a broad range of topics, including, but not limited to:
- Data Mining and Knowledge Discovery
- Machine Learning and Deep Learning
- Clustering, Classification, and Pattern Mining
- Generative AI and Foundation Models
- Large Language Models and Agentic AI
- Representation Learning, Transfer Learning, and Generalisation
- Data-Centric AI and Data Preparation
- Supervised Learning, Semi-Supervised Learning, Few-Shot and Zero-Shot Learning, Active Learning
- Reinforcement Learning and Recommender Systems
- Learning on Structured Data, Graphs, and Multi-Modal Data
- Causal Learning and Inference
- Anomaly Detection
- Learning in Streams and Time Series
- Information Retrieval, Knowledge Extraction, and Big Data Technologies
- Explainable, Fair, Trustworthy, and Responsible AI
- Privacy-Preserving and Federated Learning
- Interdisciplinary Research on Data Science Applications

For a more comprehensive list of topics covered by all four tracks, please visit: https://www.pakdd2027.org/pages/calls/research

---------------------------------------------------------------------------------------
Submission and Publication
---------------------------------------------------------------------------------------
Accepted papers will be presented at PAKDD 2027 in Wellington, New Zealand. At least one author of each accepted paper must register for and attend the conference to present the paper.

All submitted papers must follow the Springer LNCS/LNAI format and will undergo double-blind peer review. Accepted papers will be published in Springer’s Lecture Notes in Artificial Intelligence (LNAI) series.

For submission guidelines and track-specific requirements, please visit: https://pakdd2027.org/pages/calls/authors-kit

---------------------------------------------------------------------------------------
Conference Organisation
---------------------------------------------------------------------------------------
General Chairs
- Heitor Murilo Gomes, Victoria University of Wellington, New Zealand
- Albert Bifet, Télécom Paris / University of Waikato, France / New Zealand
- Longbing Cao, Macquarie University, Australia

Research Track Program Chairs
- Philippe Fournier-Viger, Shenzhen University, China
- Yun Sing Koh, University of Auckland, New Zealand
- Jingrui He, University of Illinois at Urbana-Champaign, United States

Applied Data Science Track Program Chairs
- Slawomir Nowaczyk, Halmstad University, Sweden
- Carlos Soares, University of Porto, Portugal

Survey Track Chairs
- Jesse Read, École Polytechnique, France
- Stefan Kramer, Johannes Gutenberg University Mainz, Germany

Special Track on Large Language Models and Agentic AI in Data Science Chairs
- Wenbin Zhang, Florida International University, United States
- Mahardhika Pratama, Adelaide University, Australia

Organising Committee
https://www.pakdd2027.org/pages/organisation/committee

We look forward to welcoming you to Wellington for PAKDD 2027!