7th International Conference on Big Data, Machine Learning and IoT (BMLI 2026)

Distributed and cloud native data systems
Streaming, real time analytics and eventprocessing
AI native databases, vector search and semantic retrieval
Foundation models, multimodal AI and self supervised learning
Transformers, MoE models and efficient inference
Generative AI, diffusion models and synthetic data
RAG systems, LLM augmentation and knowledge integration
LLM agents, autonomous AI and tool use models
Graph ML, GNNs and large scale graph systems
Federated, distributed and privacy preserving ML
Causal ML, counterfactual reasoning and robust inference
Continual, lifelong and meta learning
Neural architecture search and automated ML
Multi agent reinforcement learning and cooperative AI
AI safety, alignment and adversarial robustness
Large scale training, parallel ML and AI compilers
ML for code, program synthesis and automated reasoning
Knowledge graphs, semantic systems and reasoning
Multimodal analytics: text, vision, audio, sensor data
Neural fields (NeRFs, Gaussian Splatting) and 3D reconstruction
Embodied AI, robotic foundation models and simulation
Next gen IoT: 5G/6G, LPWAN, satellite IoT and NTN
Ultra low power IoT, neuromorphic edge and energy harvesting
Wireless and RF sensing, CSI based perception
IoT localization, tracking and cooperative perception
Digital twins, cyber physical systems and industrial IoT
LLMs at the edge (TinyLLMs, EdgeLLMs)
LLM powered IoT orchestration and autonomous device control
RAG for sensor data, time series and IoT streams
AI for cybersecurity, threat intelligence and LLM red teaming
Quantum ML, SciML and AI for scientific discovery
AI for climate, sustainability and environmental systems
Spatial computing, AR/VR/XR and neural rendering
Responsible AI: fairness, ethics and governance