Workshops
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 (
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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 (
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Website: TBD
Workshop 4: Operational AI for Climate and Coastal Hazards, and Public Safety
This workshop highlights the gap between AI research and operational end-to-end systems for coastal policy and public safety through real-world case studies and applications. It focuses on trustworthy and multimodal AI for climate and ocean risk, coastal hazards, and public safety, with an emphasis on beach hazard detection, early warning systems, and short-term risk forecasting.
The workshop addresses challenges associated with modern coastal and ocean datasets, which are heterogeneous, multi-source, and temporally irregular, including shoreline imagery, oceanographic grids, and environmental observations. Topics include deep learning, generative AI, and spatio-temporal methods for hazard detection, short-term and long-term forecasting, and the design of deployable end-to-end coastal safety systems.
Organizers:
- Dr. Mandana Ghanavati, UNSW Sydney / University of Melbourne (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Prof. Haoyu Jiang, Shenzhen University (
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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 (
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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
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 (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Prof. Qi Chen, Victoria University of Wellington
- Prof. Junyu Dong, Ocean University of China
Website: https://takuzui.github.io/WIMT/
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 (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Yahui Jia, South China University of Technology (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Fengfeng Wei, South China University of Technology (
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Workshop 9: Learning-assisted Algorithm Design for Evolutionary Computation
Learning-assisted algorithm design is becoming an important direction in evolutionary computation and optimization. Recent advances in machine learning, reinforcement learning, neural optimization, meta-learning, foundation models, and large language models are creating new opportunities to automate and improve the design of optimization algorithms. This workshop will focus on learning-assisted and data-driven methods for designing, configuring, and enhancing optimization algorithms. Topics include learning to optimize, neural combinatorial optimization, meta black-box optimization, large-language-model-based algorithm design, automated algorithm selection and configuration, data-driven evolutionary algorithms, surrogate-assisted optimization, and reinforcement-learning-assisted evolutionary computation. The workshop aims to bring together researchers from evolutionary computation, machine learning, operations research, and real-world optimization applications to discuss recent progress, open challenges, and future directions in learning-assisted algorithm design.
Organizers:
- Prof. Ye Tian, Anhui University (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Shuai Shao, Anhui University (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Wenjie Qiu, South China University of Technology (
This email address is being protected from spambots. You need JavaScript enabled to view it. ) - Prof. Yuejiao Gong, South China University of Technology (
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