The 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) will take place in Hong Kong SAR, China, on June 9-12, 2026. PAKDD 2026 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:

Note: Papers related to the large language models (LLMs) could be submitted to "Special Track on LLMs for Data Science".

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, trustworthy AI, 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
* Trustworthy Machine Learning
* Fairness

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, socially interactive robots, and affective computing
* 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
* Urban computing, spatial data science and pervasive computing
* 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