The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) will take place in Sydney, Australia, on June 10-13, 2025. PAKDD 2025 is soliciting contributed technical papers for presentation at the Conference and publication in the Conference Proceedings by Springer. We solicit novel, high-quality, and original research papers that provide innovative insights into all facets of knowledge discovery and data science, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, and integrity. We also encourage visionary papers on emerging topics and application-based papers offering innovative technical advancements to interdisciplinary research and applications of data science.

Conference Topics

Topics of relevance for the conference include, but are not limited to, the following:

Theoretical Foundations

    Generative AI, quantum ML, neuro-symbolic methods and reasoning, causal reasoning, non-IID learning, OOD generalization, representation learning, mathematical and statistical foundations, information theoretic approaches, optimization method
    Theoretical foundations for fairness, safety, model explainability, and XAI

Learning Methods and Algorithms

    Clustering, classification, pattern mining and association rules discovery
    Supervised learning, semi-supervised learning, few-shot and zero-shot learning, active learning
    Reinforcement learning and bandits
    Transfer learning, federated learning
    Anomaly detection, outlier detection
    Learning in recommendation engines
    Learning in streams and in time series
    Learning on structured data, images, texts and multi-modal data
    Online learning, model adaption
    Graph mining and Graph NNs

Data Processing for Learning

    Dimensionality reduction, feature extraction, subspace construction
    Data cleaning and preparation, data integration and summarization
    Learning in real-time
    Big data technologies
    Information retrieval
    Data/entity/event/relationship extraction
    User interfaces and visual analytics

Security, Privacy, Ethics, Information Integrity and Social Issues

    Modeling credibility, trustworthiness, and reliability
    Privacy-preserving data mining and privacy models
    Model transparency, interpretability, and fairness
    Misinformation detection, monitoring, and prevention
    Social issues, such as health inequities, social development, and poverty

Interdisciplinary Research on Data Science Applications

    Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection
    Symbiotic human-AI interaction, human-agent collaboration, and socially interactive robots
    Internet of Things, logistics management, network traffic and log analysis, and supply chain management
    Business and financial data, computational advertising, customer relationship management, intrusion and fraud detection, and intelligent assistants
    Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention
    Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content
    Climate, ecological, and environmental science, and resilience and sustainability
    Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, AI-assisted programming, and scientific data