Workshop 1: Open-World Robust Machine Learning (OWRML'26)

OWRML'26 addresses the gap between closed-world assumptions in ML and open, dynamic real-world deployments. The workshop focuses on three pillars: (1) Learning from imperfect supervision (noisy labels, crowdsourcing, active learning), (2) Robustness to distribution shift & openness (OOD learning, domain generalization, test-time adaptation), and (3) Data-efficient & lifelong adaptation (continual learning, few-shot/zero-shot learning). It emphasizes algorithmic robustness, theoretical guarantees, and trustworthiness.

Organizers:

• Dr. Shao-Yuan Li, Nanjing University of Aeronautics and Astronautics, China (This email address is being protected from spambots. You need JavaScript enabled to view it.)
• Dr. Chuanxing Geng, NUAA, China
• Dr. Sheng-Jun Huang, NUAA, China

Website: https://zwl00000.github.io/

Workshop 2: AI for Transportation and Energy (AITE 2026)

AITE 2026 focuses on AI as a core enabler for prediction, control, optimization, and decision-making in complex transportation and energy systems. The workshop covers advances in deep learning, reinforcement learning, graph neural networks, LLMs, generative AI, and trustworthy AI applied to key challenges including energy storage, autonomous driving, vehicle-grid coordination, infrastructure planning, critical material supply chains, energy policy simulation, and AI energy management for data centers. It aims to connect core AI research with high-impact real-world applications and foster discussion on robust, scalable, and deployable intelligent systems.

Organizer:

• Prof. Shiqi Ou, South China University of Technology / Pazhou Laboratory, China (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Website: https://translab-scut.github.io/aite/index.html

Workshop 3: Representation Learning & Clustering (RLC'26)

RLC'26 focuses on trustworthy and multimodal approaches to representation learning and clustering. Contemporary datasets—high-dimensional, multi-view, heterogeneous, and noisy—pose significant challenges. The workshop covers deep clustering, unsupervised feature learning, graph embeddings, spectral methods, mixture models, self-supervised and contrastive learning, as well as trustworthy AI (fairness, explainability, robustness). Applications include bioinformatics, cybersecurity, computer vision, recommender systems, graph mining, NLP, and medical imaging. The workshop aims to advance algorithms that jointly optimize representation and grouping, promote trustworthy AI in unsupervised learning, and leverage GNNs and LLMs for complex data.

Organizers:

• Prof. Mohamed Nadif, Université Paris Cité / Centre Borelli CNRS, France (This email address is being protected from spambots. You need JavaScript enabled to view it.)
• Prof. Lazhar Labiod, Université Paris Cité / Centre Borelli CNRS, France (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Website: TBD

Workshop 4: AI for Coastal and Public Safety: From Early Warning Systems to Real-Time Hazard Detection and Forecasting

This workshop focuses on trustworthy and multimodal AI for coastal and public safety, emphasizing beach hazard detection, early warning systems, and short-term risk forecasting. It addresses challenges in modern coastal datasets, which are heterogeneous, multi-source, and temporally irregular, including shoreline imagery (e.g., CoastSnap), oceanographic grids, and environmental observations. The workshop covers deep learning and spatio-temporal methods for hazard detection (e.g., rip currents), short-term forecasting (e.g., bluebottle arrivals), and end-to-end system design for deployable coastal safety tools. It highlights key issues such as spatial dependencies, multimodal data fusion, and robust validation in real-world settings, aiming to bridge AI research and operational coastal decision-making through case studies and applications.

Organizers:

• Prof. Mitchell Harley, UNSW, Australia (This email address is being protected from spambots. You need JavaScript enabled to view it.)
• Dr. Mandana Ghanavati, UNSW Sydney / University of Melbourne (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Website: TBD

Workshop 5: Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW 2026)

This workshop focuses on knowledge acquisition, data engineering, and machine intelligence, emphasizing both theoretical foundations and practical implementations in the era of applied AI. With the rapid adoption of AI across industries and society, from organizational decision-making to everyday intelligent devices, new opportunities emerge for acquiring and modeling knowledge from human behavior, large-scale data, and interactive systems. The workshop covers multidisciplinary approaches including knowledge engineering, machine learning, natural language processing, human-computer interaction, and applied data science, with a strong emphasis on real-world applications and system deployment. It also highlights industrial and social impacts of AI-driven knowledge systems, encouraging submissions on implemented applications, data platforms, and lessons learned from real-life deployment in domains such as economics, social networks, and sociology, aiming to bridge methodological advances with practical, socially impactful AI systems.

Workshop Chairs:

• Dr. Shiqing Wu, City University of Macau, Macau SAR (This email address is being protected from spambots. You need JavaScript enabled to view it.)
• Dr. Weihua Li, Auckland University of Technology, New Zealand (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Honorary Chairs:

• Prof. Paul Compton, University of New South Wales, Australia
• Prof. Hiroshi Motoda, Osaka University, Japan

Advisory Committee:

• Prof. Maria R Lee, Shih Chien University, China
• Prof. Kenichi Yoshida, University of Tsukuba, Japan
• Prof. Byeong Kang, University of Tasmania, Australia
• Prof. Deborah Richards, Macquarie University, Australia
• A/Prof. Quan Bai, University of Tasmania, Australia
• Dr. Qing Liu, Data61, CSIRO, Australia

Website: https://pkawwebsite.github.io/2026

Workshop 6: The 3rd International Workshop on Educational Artificial Intelligence (IWEAI 2026)

The 3rd International Workshop on Educational Artificial Intelligence (IWEAI 2026) is dedicated to exploring the transformative impact of AI in education. Within the broader field of AI, educational AI plays a significant role, and this workshop will make a meaningful contribution to showcase Educational AI Applications, promote Ethical AI Practices, and encourage Multidisciplinary Insights. We aim to bring together leading researchers, educators, and technologists to discuss the latest advancements, ethical considerations, and practical applications of AI in educational settings.

Organizers:

• Prof. Yuncheng Jiang (Co-Chair), South China Normal University
• Prof. Gang Li (Co-Chair), Deakin University

Website: https://iweai.github.io/iweai-website/2026

Workshop 7: Intelligent Marine Technology

This workshop focuses on the intersection of artificial intelligence and marine science, bringing together researchers and practitioners working on intelligent systems for ocean environments. With increasing global attention on ocean sustainability and exploration, AI plays a key role in enhancing autonomy, decision-making, and operational efficiency in complex underwater and maritime settings. The workshop covers machine learning, computer vision, reinforcement learning, and swarm intelligence for marine robotics and ocean data analysis, including autonomous underwater vehicles (AUVs), underwater object recognition, intelligent sensor networks, oceanographic data processing, marine environmental forecasting, and smart aquaculture systems. It also welcomes contributions on AI-driven solutions for fisheries, seafood production, and marine farming environments, aiming to address both scientific challenges and real-world deployment in marine technology.

Organizers:

• Prof. Gai-Ge Wang, Ocean University of China
• Prof. Qi Chen, Victoria University of Wellington
• Prof. Junyu Dong, Ocean University of China

Website: TBD

Workshop 8: Computational Intelligence for Combinatorial Optimization

This workshop explores the intersection of Artificial Intelligence, Computational Intelligence, and Combinatorial Optimization, focusing on addressing large-scale, dynamic, uncertain, multimodal, and lifelong optimization problems in real-world systems. It highlights recent advances in evolutionary computation, machine learning, reinforcement learning, and large language models for solving complex combinatorial optimization tasks. The workshop emphasizes the transition from theoretical models to practical applications in domains such as logistics, robotics, scheduling, and circuit routing, where intelligent decision-making under uncertainty is critical. By bridging algorithmic research and industrial challenges, it aims to provide a collaborative platform for researchers to share methods and insights on scalable and robust optimization frameworks with real-world impact.

Organizers:

• Ting Huang, Xidian University
• Yahui Jia, South China University of Technology
• Fengfeng Wei, South China University of Technology

Website: TBD