The ICIC 2026 Program Committee is inviting proposals for special sessions to be held during the conference (http://www.ic-icc.cn/2026/index.php), taking place on July 22-26, 2026, in Toronto, Canada.
Each special session proposal should be well motivated and should consist of 8 to 12 papers. Each paper must have the title, authors with e-mails/web sites, and as detailed an abstract as possible. The special session organizer(s) contact information should also be included. All special session organizers must obtain firm commitments from their special session presenters and authors to submit papers in a timely fashion (if the special session is accepted) and, particularly, present them at the ICIC 2026. Each special session organizer will be session chair for their own special sessions at ICIC 2026 accordingly. All planned papers for special sessions will undergo the same review process as the ones in regular sessions. All accepted papers for special sessions will also be published by Springer's Lecture Notes in Computer Sciences (LNCS)/ Lecture Notes in Artificial Intelligence (LNAI)/ Lecture Notes in Bioinformatics (LNBI).
All the authors for each special session must follow the guidelines in CALL FOR PAPERS to prepare your submitted papers.
Proposals for special sessions should be submitted in ELECTRONIC FORMAT by http://www.ic-icc.cn/icg/index.php at Special Session.
|
orders |
Title |
Organizers |
Nationality |
|
Neural Signals and Intelligent Computing: From Brain Data to Trustworthy Human-AI Collaboration |
Ziyu Jia |
China |
|
|
Advances in Graph Machine Learning |
Zhipeng
Li |
China |
1. Neural Signals and Intelligent Computing: From Brain Data to Trustworthy Human-AI Collaboration
Organizer:
Ziyu Jia
Institute of Automation, Chinese Academy of Sciences
Email: ziyu.jia.editor@outlook.com
Roger Mark
Massachusetts Institute of Technology
Email: rogermark.mit@gmail.com
Idris Elbakri
Kyrgyz National University
Email: ldris@buu.edu.kg
Scope and Topics:
This session targets the full pipeline of neural signals and intelligent
computing, spanning data acquisition and quality control; representation
learning and time--frequency and spatiotemporal modeling; cross-subject,
cross-session, and cross-device generalization; robustness, uncertainty
estimation, and interpretability; and system deployment for online decoding and
closed-loop control. Modalities include EEG, MEG, fNIRS, and ECoG, optionally
combined with peripheral physiology and behavioral streams such as EDA, ECG,
skin temperature, respiration, eye tracking, motion capture, and kinematics. We
welcome theoretical and algorithmic advances as well as end-to-end systems,
wearables and edge inference, neuromorphic or event-driven sensing and
computing, real-time human--computer interaction, and safety/compliance
practices. Evaluation should emphasize cross-dataset and cross-protocol
validation, robustness under distribution shift, personalization and few-shot
adaptation, external validation, and audits for fairness and privacy.
Application domains include cognitive and affective state estimation, attention
and memory modeling, motor imagery and assistive communication,
neurorehabilitation and neuromodulation, driving and industrial safety,
surgical guidance, and education and immersive interaction. Strong
reproducibility is encouraged through open data and code, standardized benchmarks,
clear task definitions with statistical reporting, and risk governance aligned
with ethical and legal requirements.
2. Advances in Graph Machine Learning
Organizers:
Zhipeng Li
Ningbo Institute of Digital Twin
Email: lizhipengqilu@gmail.com
Ming Li
Zhejiang Normal University
Email: mingli@zjnu.edu.cn
Yun Ding
Anhui University
Email: yunding92@163.com
Xuesong Jiang
Qilu University of Technology(Shandong Academy of Sciences)
Email: jxs@qlu.edu.cn
Bo Jiang
Anhui University
Email: jiangbo@ahu.edu.cn
Zhuhong You
Northwestern Polytechnical University
Email: zhuhongyou@nwpu.edu.cn
Scope and Topics:
Special Session on “Advances in Graph Machine Learning” Graph machine learning
has become one of the most active research frontiers in artificial
intelligence, providing powerful tools for representing, learning, and
reasoning over complex relational structures. Graph-based models have
demonstrated strong capabilities in diverse domains such as social networks,
biological systems, chemistry, recommendation, and multimodal information
fusion. With the rapid progress of deep learning, graph neural networks (GNNs),
graph transformers, and large language models (LLMs), graph machine learning
has evolved beyond traditional graph processing—empowering intelligent agents,
multimodal reasoning systems, and large-scale decision-making frameworks. This
special session aims to bring together researchers and practitioners from academia
and industry to discuss recent advances, emerging theories, scalable
algorithms, and impactful applications in graph machine learning. The session
encourages cross-disciplinary contributions that link graph learning, neural
architectures, and intelligent systems, highlighting both fundamental insights
and real-world progress. Topics of Interest Topics of interest include, but are
not limited to:
√ Learning theory, expressivity, and generalization of GNNs and graph
transformers
√ Spectral, spatial, and multiscale perspectives on graph learning
√ Novel architectures and learning paradigms for GNNs, hypergraph neural
networks, and manifold learning
√ Scalable, efficient, and distributed algorithms for large and dynamic graphs
√ Self-supervised, contrastive, or foundation models for graph representation
learning
√ Graph-based reasoning and planning for intelligent agents and multi-agent
systems
√ Graph-structured environments for task decomposition, collaboration, and
control
√ Integration of graph knowledge into LLMs and multimodal reasoning frameworks
√ Graph learning in molecular discovery, drug design, and bioinformatics
Objective This special session provides a timely platform to exchange the
latest findings, foster interdisciplinary collaboration, and identify future
directions in graph machine learning. It welcomes both theoretical and
application-oriented studies that advance the understanding of relational,
geometric, and structured data learning.