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

BDML 2026

URL: https://www.bdml2026.org/
Event Date: 2026-06-27 ~ 2026-06-28
Submission Date: 2026-04-04
Location: Copenhagen, Denmark

Computer Security & Cryptography Computer Vision & Pattern Recognition Computing Systems Engineering & Computer Science (General) Human Computer Interaction Theoretical Computer Science

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

June 27 ~ 28, 2026, Copenhagen, Denmark

https://www.bdml2026.org/

Scope

7^th 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.

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 April 04, 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).