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

Name: 7th International Conference on Big Data and Machine Learning (BDML 2026)
Start: 2026-06-27T10:00:00+02:00
End: 2026-06-28T10:00:00+02:00
Location: Copenhagen

Schedule

Sat, 27 Jun, 2026 at 10:00 am to Sun, 28 Jun, 2026 at 10:00 am

UTC+02:00

Location

Copenhagen | Copenhagen , SK

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 May 30, 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.