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AIMLA 2026 : 6th International Conference on AI, Machine Learning and Applications

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Link: https://www.ccnet2026.org/aimla/index
 
When Mar 21, 2026 - Mar 22, 2026
Where Sydney, Australia
Submission Deadline Jan 31, 2026
Notification Due Feb 10, 2026
Final Version Due Feb 17, 2026
Categories    artificial intelligence   machine learning   robotics   computer vision
 

Call For Papers

6th International Conference on AI, Machine Learning and Applications (AIMLA 2026)

March 21 ~ 22, 2026, Sydney, Australia

Hybrid -- Registered authors can present their work online or face to face.

Scope & Topics

6th International Conference on AI, Machine Learning and Applications (AIMLA 2026) serves as a premier global forum for presenting and exchanging the latest advances in Artificial Intelligence, Machine Learning, and their rapidly expanding range of real world applications. AIMLA 2026 brings together leading researchers, innovators, and industry practitioners to share breakthroughs in theory, algorithms, methodologies, and system level implementations that are shaping the future of intelligent technologies.

The conference welcomes high impact contributions across all major areas of AI and ML spanning foundational research, applied innovations, and interdisciplinary developments. By fostering collaboration between academia and industry, AIMLA 2026 aims to provide a dynamic platform for discussing emerging challenges, exploring transformative ideas, and showcasing cutting edge progress that drives the next generation of intelligent systems.

Topics of interest include, but are not limited to, the following

    Foundations of AI & Machine Learning

  • Machine Learning Theory & Optimization
  • Statistical Learning & Generalization
  • Probabilistic Modeling & Bayesian Methods
  • Causality, Counterfactual Reasoning & Causal ML
  • Trustworthy, Explainable & Interpretable AI (XAI)
  • Fairness, Accountability & Ethics in AI

    Deep Learning & Representation Learning

  • Deep Neural Architectures & Training Techniques
  • Self Supervised, Semi Supervised & Weakly Supervised Learning
  • Generative Models (GANs, Diffusion Models, VAEs)
  • Foundation Models & Large Scale Pretraining
  • Multimodal Learning (vision language, audio text, sensor fusion)
  • Continual, Lifelong & Transfer Learning

    Natural Language Processing & Speech Technologies

  • Large Language Models (LLMs) & Instruction Tuning
  • Text Generation, Summarization & Reasoning
  • Speech Recognition, Synthesis & Spoken Dialogue Systems
  • Multilingual & Low Resource NLP
  • Responsible & Safe Language Models

    Computer Vision & Perception

  • Image/Video Understanding & Scene Analysis
  • Vision Transformers & Diffusion Based Vision Models
  • 3D Vision, Reconstruction & Robotics Perception
  • Multimodal Vision Language Models
  • Medical Imaging & Scientific Vision Applications

    Reinforcement Learning & Decision Making

  • Deep RL, Offline RL & Safe RL
  • Multi Agent Systems & Game Theoretic Learning
  • Planning, Control & Sequential Decision Making
  • RL for Robotics, Autonomous Systems & Real World Deployment

    Applied AI & Domain Specific Intelligence

  • AI for Healthcare, Bioinformatics & Computational Biology
  • AI for Finance, Climate, Sustainability & Energy
  • AI for Education, Social Good & Public Policy
  • Scientific Machine Learning & Physics Informed Models
  • AI for Smart Cities, IoT & Cyber Physical Systems

    Robotics, Autonomous Systems & Embodied AI

  • Robot Learning & Adaptive Control
  • Embodied AI, Simulation & Digital Twins
  • Human Robot Interaction & Assistive Robotics
  • Autonomous Vehicles, Drones & Navigation

    Data Science, Knowledge Systems & Information Retrieval

  • Large Scale Data Mining & Knowledge Discovery
  • Knowledge Graphs, Semantic Reasoning & Ontologies
  • Information Retrieval, Search & Recommender Systems
  • Vector Databases & Embedding Based Retrieval

    AI Systems, Hardware & Scalability

  • Distributed & Parallel Training Systems
  • Efficient AI: Model Compression, Quantization & Pruning
  • Edge AI, TinyML & On Device Learning
  • Neuromorphic Computing & AI Accelerators
  • Software/Hardware Co Design for ML Workloads

    Emerging Topics & Frontier Research

  • AI Safety, Alignment & Robustness
  • Adversarial ML & Secure AI Systems
  • Synthetic Data Generation & Data Centric AI
  • Human AI Collaboration & Cognitive Modeling
  • Autonomous Agents & Multi Modal Reasoning
  • Benchmarking, Evaluation & Reproducibility in AI

Paper Submission

Authors are invited to submit papers through the conference Submission System by January 31, 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 The proceedings of the conference will be published by Computer Science Conference Proceedings (H index 45) in Computer Science & Information Technology (CS & IT) series (Confirmed).

Selected papers from AIMLA 2026, after further revisions, will be published in the special issue of the following journals.

Important Dates


Submission Deadline: January 31, 2026
Authors Notification: February 10, 2026
Final Manuscript Due: February 17, 2026

Co - Located Event

***** The invited talk proposals can be submitted to aimla@ccnet2026.org


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