7th International Conference on Big Data and Machine Learning (BDML 2026)

Memo

7th International Conference on Big Data and Machine Learning (BDML 2026) June 27 ~ 28, 2026, Copenhagen, Denmark https://bdml2026.org/index Scope The 7th 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. BDML 2026 welcomes high quality contributions that display original research results, visionary projects, comprehensive surveys and real world industrial experiences. Submissions are encouraged from all areas of Big Data and Machine Learning, particularly those that demonstrate significant advances in theory, systems, algorithms and applications. Topics of interest include, but are not limited to, the following 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 Paper Submission Authors are invited to submit papers through the conference Submission System by June 13, 2026 (Final Call). 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). Selected papers from BDML 2026, after further revisions, will be published in the special issue of the following journal. Information Technology in Industry (ITII) International Journal of Data Mining & Knowledge Management Process (IJDKP) International Journal of Database Management Systems (IJDMS) Machine Learning and Applications: An International Journal (MLAIJ) Advances in Vision Computing: An International Journal (AVC) International Journal of Grid Computing & Applications (IJGCA) Important Dates (2nd batch : submissions after May 11th) Submission Deadline: June 13, 2026 (Final Call) Authors Notification: June 20, 2026 Registration & Camera-Ready Paper Due: June 22, 2026 Contact Us Here’s where you can reach us :This email address is being protected from spambots. You need JavaScript enabled to view it. (or) This email address is being protected from spambots. You need JavaScript enabled to view it.