ICDDS 2025 invites high-quality original contributions from researchers, engineers, and subject matter experts from academia, industry, and start-ups to present their latest innovations in Data/Decision Systems (DDS) and their applications. The conference provides a unique forum for sharing interdisciplinary research, system design, and real-world applications grounded in data and decision science.

Rapid advances and growth in the connected world across different application domains are fast pacing the data volumes generated from a range of devices. Digital transformation and the systematic adoption of advanced Internet of Things (IoT), Cyber Physical Systems, and Smart Things further endorse such high growth. Data being the centre of innovative systems, its overall realization in solution building depends on several aspects such as data quality, data management, computation, knowledge discovery, decision support, etc. ICDDS 2025 aims to look broadly at data-based innovation and technologies in interdisciplinary areas. The conference is particularly interested in all aspects of data and decision science, applications, solution design, and system challenges in new paradigms, ranging from theory to applied research.

The areas of interest are broadly categorized into the following three streams:

(1) Foundational Research: theory to novel findings in all aspects of Data/Decision science

The topics of interest include, but are not limited to:

● Probabilistic Inference
● Online Learning
● Generative Models
● Large Language Models
● Data privacy
● New Computational Models for Big Data
● New Data Standards
● Multi-agent systems
● ML and Deep learning
● Data Mining and Optimization
● Text Analytics and NLP
● Time-series Analysis
● Information Retrieval
● Semantic-based Data Mining and Data Pre-processing
● Machine Reasoning and Hybrid AI

(2) Systems Research: system aspects of data/decision science

The topics of interest include, but are not limited to:

● CPU/GPU architectures for AI/ML applications
● VLSI and architecture design for data science applications
● Compiler optimization using AI/ML
● Algorithms and systems for database
● Systems for resource-constrained computing
● Systems for Big Data
● Energy-efficient computing and Inferencing architectures
● Software techniques and architectures in Cloud/Grid/Stream Computing
● Algorithms and systems for database
● Resource-constrained computing architectures

(3) Applications: real-world problems and their solution approach using data/decision science

● Data Science Use Cases
● Recommender Systems
● Bias and fairness
● Algorithm explainability
● Privacy, security, and trust
● Scalability
● Blockchain-based applications
● Data transparency
● Quantum-safe applications
● Lessons from real-world deployments
● Urban computing and analytics
● Multimedia Processing
● Social computing and analytics