About ICDLA 2026

In an era where information technology is advancing at an unprecedented pace, data has emerged as the cornerstone of societal progress and scientific discovery. However, the capacity of single-modality data—such as plain text or static images—to convey complex information is increasingly approaching its limits. The next paradigm shift in artificial intelligence is widely seen to lie in the deep integration and cross-disciplinary innovation of multimodal learning, machine learning, and data science.

To convene global expertise in exploring the future of this interdisciplinary domain, the 2026 International Conference on Multimodality, Machine Learning and Data Science(MMLDS 2026) will be held in Zhengzhou, China, from October 30 to November 1, 2026. This conference aims to establish an international platform for scholars, engineers, and industry leaders worldwide to engage in in-depth discussions on core topics, including multimodal perception and understanding, machine learning theory and methodologies, data science and intelligent systems, and cutting-edge applications.

The topics of interest for submission include, but are not limited to:

◕Track1:   Multimodal Learning & Artificial Intelligence

Image Understanding
Video Analysis
Speech Recognition & Processing
Text & Language Modeling
Cross-Modal Retrieval
Multimodal Fusion Techniques
Affective Computing & Cognitive Analysis
Medical Image Analysis
Natural Language Processing
Generative Models
Intelligent Human-Computer Interaction
Intelligent Recommendation Systems
Autonomous Driving & Perception
Visual Question Answering
Modality Transformation & Synthesis

◕Track2:  Data Science & Big Data Analytics

Data Preprocessing & Cleaning
Data Mining Techniques
Big Data Management
Data Visualization
Data Integration & Modeling
Data-Driven Decision Making
Spatiotemporal Data Analysis
Network Data Analysis
Social Computing & Behavioral Analysis
Business Intelligence
Data Security & Privacy
Cloud Computing & Data Processing
High-Performance Data Analytics
Data Science Methodology
Data-Driven Scientific Research

◕Track3:  Machine Learning & Deep Learning

Supervised Learning
Unsupervised Learning
Reinforcement Learning
Self-Supervised Learning
Deep Neural Networks
Graph Neural Networks
Sequence Modeling
Model Compression & Optimization
Transfer Learning
Federated Learning
Explainable AI
Anomaly Detection
Time Series Prediction
Model Evaluation & Selection
Automated Machine Learning (AutoML)

Publication

All accepted full papers will be published in the conference proceedings and will be submitted to EI Compendex / Scopus  for indexing.