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

 

 

 

 

7th International Conference on Big Data and Machine Learning (BDML 2026) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.

 

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Big Data and Machine Learning. 

 

 

·       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

·       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

·       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

·       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)

·       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

·       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

·       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

·       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

·       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

·       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

 

Authors are invited to submit papers through the conference Submission System by May 16, 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 Proceedingsin 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.

 

·       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)

·       Information Technology in Industry (ITII)

 

·       Submission Deadline: May 16, 2026

·       Authors Notification: June 13, 2026

·       Registration & camera - Ready Paper Due: June 20, 2026

 

 

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