DSIT 2026 welcomes relevant paper submissions from researchers in academia, industry, and government, such as students, engineers, practitioners, scientists, and policy makers. We welcome paper submissions with original technical and scientific research results in relevant topics.

 

Main Topics of Interest:

 

Track 1: Foundations & Methodologies for Data Science and AI
• Data Science Foundations: Theory of Data Science, Data and Knowledge Representation, Data Quality and Provenance, Data Semantics and Ontology.
• Machine Learning & Statistical Learning: Supervised/Unsupervised/Semi-Supervised Learning, Deep Learning Architectures, Reinforcement Learning, Causal Inference and Discovery, Statistical Methods for Data Science.
• Data Mining & Knowledge Discovery: Pattern Discovery and Recognition, Graph Mining and Network Analysis, Frequent Pattern Mining, Outlier and Anomaly Detection, Text and Web Mining.
• Big Data Analytics & Algorithms: Streaming Data Processing and Analytics, High-Dimensional Data Analysis, Parallel and Distributed Data Mining, Spatial-Temporal Data Analysis.
• Multimodal & Advanced Analytics: Multimodal Data Fusion, Computer Vision, Natural Language Processing, Speech and Audio Processing.
Track 2: Systems, Infrastructure, and Engineering for Data
• Data Management & Warehousing: Database Systems and Architectures, Data Warehousing, OLAP, Information Retrieval, Recommendation Systems.
• Data Engineering & MLOps: DataOps, MLOps, ETL Pipelines, Feature Stores, Data Versioning, and Workflow Management.
• Computing Infrastructure: Cloud, Fog, and Edge Computing for Data, High-Performance Computing (HPC) for Big Data, Serverless Computing.
• Data Privacy & Security: Privacy-preserving Data Publishing, Differential Privacy, Federated Learning, Data Security and Cryptography.
• Data Visualization & Interaction: Visual Analytics, Interactive Data Exploration, Immersive Data Visualization (AR/VR).
Track 3: Data-Driven Applications and Societal Impact
• Smart Cities & IoT: IoT Data Management, Smart City Data Platforms, Urban Computing.
• Healthcare & Bioinformatics: Medical Image Analysis, Health Informatics, Computational Biology, Genomics.
• Finance & Business Analytics: Fintech, Risk Modeling, Algorithmic Trading, Customer Analytics, Business Intelligence.
• Scientific Discovery & Digital Twins: Data-intensive Scientific Computing (e-Science), Climate Data Analysis, Digital Twin.
• AI for Social Good: Fairness, Accountability, and Transparency in AI (FAccT), Ethical AI, AI for Sustainability/Green IT.
• Other Novel Applications: Agriculture, Intelligent Transportation, Industrial IoT.
Special Session I:
Generative AI and Foundation Models: LLMs and Prompt Engineering, AIGC (image/video/code generation), Data-centric AI, where models optimize datasets.
Special Session II:
Responsible AI and Green Computing: Model Explainability (XAI), Bias Detection and Mitigation, Energy-efficient Model Training, Carbon-aware Computing.