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 |
|
|
Advances in Multimodal Intelligence for Visual and Medical Data |
Meng Xing |
China |
|
|
Computational Intelligence Models for Smart Cities |
Pengjiang
Qian |
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:
1) Learning theory, expressivity, and generalization of GNNs and graph
transformers
2) Spectral, spatial, and multiscale perspectives on graph learning
3) Novel architectures and learning paradigms for GNNs, hypergraph neural
networks, and manifold learning
4) Scalable, efficient, and distributed algorithms for large and dynamic graphs
5) Self-supervised, contrastive, or foundation models for graph representation
learning
6) Graph-based reasoning and planning for intelligent agents and multi-agent
systems
7) Graph-structured environments for task decomposition, collaboration, and
control
8) Integration of graph knowledge into LLMs and multimodal reasoning frameworks
9) 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.
3. Advances in Multimodal Intelligence for Visual and Medical Data
Organizer:
Meng Xing
Ningbo Institute of Digital Twin, Eastrn Institute of Technology
Email: mxing@idt.eitech.edu.cn
Yong Su
Tianjin Normal University
Email: suyong@tju.edu.cn
Mingliang Dou
Taiyuan University of Technology
Email: doumingliang@tyut.edu.cn
Yao Zhang
Tianjin University
Email: zzyy@tju.edu.cn
Yude Bai
Tiangong University
Email: baiyude@tiangong.edu.cn
Zehua Zhang
Scientific and Technological Innovation Center
Email: zehua_new@yeah.net
Scope and Topics:
Multimodal intelligent computing has emerged as a key direction in modern
artificial intelligence, leveraging heterogeneous data from diverse sources.
These sources include visual data (images, videos, depth maps, RGB–IR, etc.),
textual information, audio, sensor measurements, and clinical or biomedical
data. Integrating and reasoning over these heterogeneous modalities enables
richer understanding, improved perception, and more robust decision-making.
This special session seeks high-quality contributions on advances in multimodal
intelligence, covering both traditional visual applications and medical/clinical
data scenarios. By highlighting the synergy between method innovation and
application, this session provides a platform for cross-domain insights,
bridging foundational multimodal learning techniques with practical visual and
medical use cases. Topics of Interest (include but not limited to): 1) Joint
representation learning and cross-modal embedding
2) Generative models for multimodal data (diffusion models, VAEs,
autoregressive models)
3) Object detection, segmentation, classification, captioning, and reasoning
with multimodal cues
4) Multimodal datasets, benchmarks, and evaluation protocols
5) Medical imaging combined with clinical text, omics, pathology, or
physiological signals
6) Disease diagnosis, prognosis modeling, treatment planning, and clinical
decision support
7) Multimodal image reconstruction, enhancement, segmentation, and detection
8) Report generation, visual–textual grounding, and structured reasoning
9) Digital health, wearable sensors, remote monitoring, and real-world clinical
validation
10) Robustness, interpretability, safety, and fairness of multimodal medical AI
4. Computational Intelligence Models for Smart Cities
Organizer:
Pengjiang Qian
Jiangnan University
Email: qianpjiang@jiangnan.edu.cn
Wenbing Zhao
Cleveland State University
Email: w.zhao1@csuohio.edu
Khin-Wee Lai
University of Malaya
Email: lai.khinwee@um.edu.my
Scope and Topics:
Smart city comprehensive adopts the new generation of Internet, big data,
Internet of Things, artificial intelligence, cloud computing and other
information technologies to realize the intelligence of urban construction,
planning, management, and service, forming an innovative and sustainable
intelligent city. It integrates a variety of new generation information
technologies to complete the automatic perception, collection, integration,
analysis and sharing of urban information resources, and realize intelligent
medical care, emergency response, environmental protection, education,
transportation, etc., thus bringing convenience, high-efficiency, intelligence
to people's life and response to their personalized needs. The construction of
smart city involves many aspects, from urban road traffic to urban spatial
layout and management, which require high technical support, as well as a
complete information-based decision-making mechanism to provide a reliable
guarantee for urban development.
In recent years, advanced computational intelligence models such as deep
learning, active learning, transfer learning and information fusion have
brought opportunities for smart city. Computational intelligence models have
been successfully applied in many areas of smart city construction, such as
urban traffic flow prediction, health monitoring and early warning, mobile
intelligent question answering system, intelligent environmental resource deployment,
etc. Although the existing computational intelligence models based on
single-view data have achieved certain results, their practical application
performance still cannot meet the needs of smart city construction. Compared
with single-view data, multi-view data can provide more abundant and
comprehensive information for the computational intelligence models, thereby
further improving the performance of the model. Therefore, it is necessary to
study the deep multi-view learning-driven computational intelligence model to
overcome the defects existing in the construction of smart cities.
In this special issue, we hope to build a platform for researchers and
engineers to explore this field and contribute their experience and wisdom to
the development of computational intelligence models for smart cities. Topics
of intended papers contain, but not limited to,
1) Advanced computational intelligence models for smart city, such as deep
learning, sparse learning, transfer learning, active learning, multi-task
learning
2) Smart city information management platform involving artificial intelligence
3) Smart city information decision-making system based on multi-view data
4) Prediction models combined with multi-view features, such as intelligent
traffic flow prediction, intelligent medical disease prediction, intelligent
weather prediction, signal light warning
5) Visualized human-computer interaction platform for smart city
6) Smart city monitoring system driven by deep multi-view learning
7) Deployment and management for smart cities with unsupervised methods, such
as self-training models, clustering algorithms, principal component analysis
8) Data automated management and analysis for smart city
9) Screening and fusion of multi-modal heterogeneous clinical data for smart
medical care with supervision methods, such as random forest, decision tree,
naive bayes
Design of smart city resource allocation system based on big data analysis