The 7^th International Conference on Big Data and Machine Learning (BDML 2026) brings together researchers, practitioners and industry leaders to explore the rapidly evolving landscape of data driven intelligence. As Big Data and Machine Learning continue to transform science, engineering, business and society, BDML 2026 serves as a premier venue for presenting innovative ideas, breakthrough methodologies and innovative applications that push the boundaries of what intelligent systems can achieve. The conference provides a dynamic environment for discussing emerging challenges, sharing novel solutions and shaping the future directions of the field.

Submission Guidelines

Authors are invited to submit papers through the conference Submission System by March 21, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

List of Topics

• Foundation Models, Generative AI and Multimodal Systems
• Large Language Models (LLMs): architectures, scaling laws, training, alignment
• Multimodal foundation models (vision language, audio text, video language)
• Retrieval Augmented Generation (RAG) and knowledge grounded AI
• Efficient fine tuning, distillation, quantization and model compression
• Diffusion models and generative modeling for images, audio, video and 3D
• Safety, robustness and evaluation of foundation models
• Machine Learning Theory, Algorithms and Optimization
• Optimization methods for deep and large scale models
• Representation learning and self supervised learning
• Probabilistic modeling, Bayesian methods and uncertainty quantification
• Meta learning, few shot learning and transfer learning
• Online, continual and lifelong learning
• Causal inference, causal discovery and counterfactual reasoning
• ML Systems, Infrastructure and Scalable Computing
• Distributed training systems, parallelization strategies and scheduling
• ML compilers, accelerators and hardware -software co design
• Cloud native, edge and serverless ML systems
• High performance computing for ML and data intensive workloads
• Inference optimization, serving systems and low latency ML pipelines
• Energy efficient ML, Green AI and sustainable computing
• Big Data Systems, Management and Engineering
• Scalable data processing architectures and dataflow systems
• Data engineering, pipelines, orchestration and workflow automation
• Data integration, cleaning, quality and governance
• Real time and streaming data analytics
• Data compression, indexing and query optimization
• Privacy preserving data management (DP, MPC, HE)
• Data Mining, Knowledge Discovery and Graph Intelligence
• Large scale data mining algorithms and theory
• Graph neural networks (GNNs) and graph representation learning
• Knowledge graphs, reasoning and graph mining
• Temporal, spatial and spatiotemporal data mining
• Anomaly detection, fraud detection and rare event modeling
• Recommender systems and personalization
• Responsible, Trustworthy and Secure AI
• Explainability, interpretability and transparency in ML
• Fairness, bias mitigation and ethical AI
• AI governance, policy and regulatory compliance
• Adversarial ML, robustness and secure model training
• Privacy preserving ML (federated learning, DP, secure aggregation)
• ML for cybersecurity and threat intelligence
• Distributed, Federated and Edge Intelligence
• Federated learning algorithms, systems and applications
• Collaborative and decentralized ML
• Edge AI, on device learning and TinyML
• 6G, IoT and cyber physical systems for ML and data analytics
• Resource constrained learning and communication efficient ML
• Autonomous Agents, RL and Decision Making
• Reinforcement learning theory and applications
• Multi agent systems and coordination
• LLM based agents and tool using AI systems
• Planning, control and sequential decision making
• Simulation based learning and digital twins
• Scientific ML, Simulation and Domain Applications
• ML for physics, chemistry, biology and materials science
• Climate modeling, environmental analytics and sustainability
• Healthcare analytics, medical AI and computational biology
• Finance, economics and risk modeling
• Smart cities, transportation and mobility analytics
• Multimedia, vision, speech and natural language analytics
• Evaluation, Benchmarking and Data Centric AI
• Dataset creation, curation and governance
• Data centric AI methodologies and tooling
• Benchmarking ML systems and reproducibility studies
• Robust evaluation protocols for large scale models
• Synthetic data generation and simulation driven datasets

Publication

he proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

Contact

Here's where you can reach us : bdml@bdml2026.org (or) bdmlconfe@yahoo.com