Workshop proposals should be submitted in English via the Conference website. You could also send the proposal (including the suggested title, a brief description, and the organizers’ information) to IAIC Secretariat iaic@techscience.com.

 

The chair will be in charge of corresponding, call for papers, instructing speakers, and will act as host and timekeeper during the session. The chair is also expected to assure speakers to present at the IAIC2026, including payment of registration fees.

 

The structure of the symposium is not fixed. Generally, for contributed papers, each will give a 15-minute presentation. For student papers, these will be of 10-minute per presentation. Sufficient time for discussion should be included.

 

Proposals will be reviewed and notification for acceptance will be sent in around two weeks after the form has been submitted.

 

If you have any questions or need any assistance, please contact the IAIC Secretariat

Email: iaic@techscience.com.

 

Electromagnetic Spectrum Intelligence (EMSI) has become a pivotal frontier in modern wireless systems, particularly with the integration of artificial intelligence (AI) for dynamic spectrum management and security. Unlike traditional spectrum analysis methods, AI-powered approaches enable real-time adaptation to complex electromagnetic environments while addressing emerging security challenges.

 

This workshop aims to explore cutting-edge innovations at the intersection of electromagnetic spectrum and AI, focusing on:

• AI/ML for real-time spectrum sensing and classification
• Adversarial attacks and defense in spectrum sharing systems
• Deep learning-based signal fingerprinting and emitter identification
• Cognitive radio networks with AI-driven spectrum allocation
• Autonomous spectrum management for 6G and satellite communications
• Quantum machine learning for spectrum optimization
• AI-enhanced anti-jamming and anti-spoofing strategies
• Multi-Modal data process and fusion
• Unmanned Systems Spectrum Intelligence
• Other related topic, etc

 

Yun Lin Harbin Engineering University, China

Yun Lin (M'14, SM'23) received the B.S. degree from Dalian Maritime University, Dalian, China, in 2003, the M.S. degree from the Harbin Institute of Technology, Harbin, China, in 2005, and the Ph.D. degree from Harbin Engineering University, Harbin, China, in 2010. He was a research scholar with Wright State University, USA, from 2014 to 2015. Now, he is currently a full professor in the College of Information and Communication Engineering, Harbin Engineering University, China. His current research interests include machine learning and data analytics over wireless networks, signal processing and analysis, cognitive radio and software defined radio, artificial intelligence and pattern recognition.

 

He had published more than 200 international peer-reviewed journal/conference papers, such as the IEEE TSP, TITS, TCOM, IoT, TVT, TCCN, INFOCOM, GLOBECOM, ICC. He is serving as an Editors-in-Chief of EAI Endorsed Transactions on Mobile Communications and Applications, editors for the IEEE TRANSACTIONS ON RELIABILITY, IEEE Internet of Things, Digital Communications and Networks, Wireless Network. He serves as GC2022 co-chair of Mobile and Wireless Networking Symposium, General Vice Chair of VTC-2021 Fall, General Chair of ADHIP 2023 and Mobimedia 2022,and TPC member of GLOBECOM, ICC, VTC, ICCC. He has gotten the best paper of ICCC 2023, ICCT2023, Mobimedia 2022, ADHIP 2021, CSPS 2018. He is a recipient of IEEE Outstanding service award of Trustcom 2021, IEEE Outstanding Track Chair Award of MASS 2021. He has been selected as IET Fellow in 2024.

Zhengwei Xu Henan Normal University, China

Dr. Zhengwei Xu is anassociate professor at Henan Normal University. His research focuses on wireless security, AI-driven signal processing, and electromagnetic intelligence. He has published over 30 papers in high-impact journals and serves as an organizer and reviewer for major IEEE conferences, including ICCT and EAI ADHIP.

Qiuming Zhu Nanjing University of Aeronautics and Astronautics, China

Qiuming Zhu received the B.S. degree in electronic engineering and the M.S. and Ph.D. degrees in communication and information systems from Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2002, 2005, and 2012, respectively. Since 2021, he has been a Full Professor in the Department of Electronic Information Engineering, NUAA. From Oct. 2016 to Oct. 2017, and for a few months from Aug. 2018 to Aug. 2024, he was a visiting scholar at Heriot-Watt University, Edinburgh, U. K. He has authored or co-authored over 200 articles in refereed journals and conference proceedings and holds over 80 international and Chinese patents. His current research interests include channel sounding, modeling, and emulation for low-altitude communication and unmanned aerial vehicles (UAV) communication systems, 3D spectrum mapping, and environmental awareness.

Guan Gui Nanjing University of Posts and Telecommunications, China

Guan Gui received the Dr. Eng degree in Information and Communication Engineering from University of Electronic Science and Technology of China, Chengdu, China, in 2012. From 2009 to 2014, he joined the wireless signal processing and network laboratory, Department of Communications Engineering, Graduate School of Engineering, Tohoku University as for research assistant as well as postdoctoral research fellow, respectively. From 2014 to 2015, he was an Assistant Professor in the Department of Electronics and Information System, Akita Prefectural University. Since 2015, he has been a professor with Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, China. His recent research interests include artificial intelligence, deep learning, non-orthogonal multiple access, wireless power transfer, and physical layer security.

 

He is an IEEE Fellow. Dr. Gui has published more than 200 international peer-reviewed journal/conference papers and won night best paper awards, e.g., ICC 2017, ICC 2014 and VTC 2014-Spring. He received Member and Global Activities Contributions Award in 2018, Top Editor Award of IEEE Transactions on Vehicular Technology in 2020. He was also selected as for Jiangsu Specially-Appointed Professor in 2016, Jiangsu High-level Innovation and Entrepreneurial Talent in 2016, Jiangsu Six Top Talent in 2018, Nanjing Youth Award in 2018. He is serving or served on the editorial boards of several journals, including the IEEE Transactions on Vehicular Technology, Physical Communication, Wireless Networks, IEEE Access, Security and Communication Networks, and KSII Transactions on Internet and Information Systems, Journal of Communications. In addition, he served as TPC Chair of WiMob 2020, Track Chair of ISNCC 2020 and VTC 2020 spring, Award Chair of PIMRC 2019, and TPC member of many IEEE international conferences, including GLOBECOM, ICC, WCNC, PIRMC, VTC, and SPAWC.

This workshop will explore the intersection of Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT), focusing on how AI technologies can enhance industrial processes, improve operational efficiency, and drive innovation in various sectors. The workshop aims to provide a comprehensive understanding of AI's role in IIoT applications and showcase cutting-edge solutions that leverage AI to solve complex industrial challenges.

 

Potential topics include:

• AI-Driven Predictive Maintenance
• Industrial Automation and Robotics
• Data Analytics and Machine Learning
• Edge Computing in IIoT
• Smart Manufacturing
• Anomaly Detection and Fault Diagnosis
• Cybersecurity for IIoT
• Human-Machine Collaboration
• Digital Twins
• AI Ethics and Governance
• Large Language Models
• Multi-modal Large Models

 

Weiwei Jiang Beijing University of Posts and Telecommunications, China

Dr. Weiwei Jiang received the B.Sc. and Ph.D. degrees from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2013 and 2018, respectively. He is currently an assistant professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, and Key Laboratory of Universal Wireless Communications, Ministry of Education. His current research interests include artificial intelligence, machine learning, big data, wireless communication and edge computing. He has published more than 60 academic papers in IEEE Trans and other journals, with more than 3900 citations in Google Scholar. He is one of 2023 and 2024 Stanford's List of World's Top 2% Scientists. In addition to his research endeavors, Dr. Weiwei Jiang actively engages with scholarly journals as an Editor, including Information Fusion, Future Generation Computer Systems, Engineering Reports, Data Science and Management, Journal of Computational and Cognitive Engineering, and Indonesian Journal of Electrical Engineering and Computer Science, INGENIERIA E INVESTIGACION, International Journal of Intelligent Transportation Systems Research, EAI Endorsed Transactions on AI and Robotics, EAI Endorsed Transactions on Industrial Networks and Intelligent Systems. He is also the Guest Editor for many journals, including IEEE Transactions on Industrial Cyber-Physical Systems, ACM Transactions on Autonomous and Adaptive Systems, Digital Communications and Networks, Information Fusion, Neural Computing and Applications, International Journal of Intelligent Systems, etc.

Weixi Gu China Academy of Industrial Internet, China

Dr. Weixi Gu received the Ph.D. degree from Tsinghua University (THU), and the Bachelor degree from Shanghai Jiao Tong University. He was the Postdoc at University of California, Berkeley. He is currently a principal researcher at China Academy of Industrial Internet (CAII). His research interests include mobile computing, Industrial Internet of Things, and machine learning.

Miao He Beijing Institute of Mathematical Sciences and Applications, China

Dr. Miao He is currently an assistant professor in Beijing Institute of Mathematical Sciences and Applications. She received her Ph.D. degree from Tsinghua University in 2020, and bachelor’s degree from China University of Political Science and Law in 2015. She was a visiting scholar in UC, Berkeley and a senior researcher in HEC, Lausanne. Her research interests lie in deep learning, information theory, and related algorithms design.

This workshop explores the artificial intelligence (AI) security, and data security addressing emerging threats to AI systems, data integrity, and privacy in increasingly automated environments. The event focuses on methodologies to secure AI-driven applications, protect sensitive data, and ensure ethical compliance across industrial domains. It aims to provide a comprehensive understanding of security frameworks for AI deployment while presenting state-of-the-art solutions for risk mitigation in complex AI ecosystems.

 

Potential topics include:

• Data Poisoning and Countermeasures
• Privacy-Preserving AI
• Post-quantum Cryptography
• Implementation Security
• Inference Attack

 

Yanbin Li Shandong University, China

Dr. Yanbin Li received the Ph.D degree in School of Cyber Science and Engineering from Wuhan University. He is an associate professor at the School of Software, Shandong University. He received the Program of Taishan Young Scholars of Shandong Province. His research interests include side-channel analysis, AI security and cryptography.

Yongjun Ren Nanjing University of Information Science and Technology, China

Prof. Ren obtained the PhD degree in the computer and science department at the NanJing University of Aeronautics and Astronautics, China, in 2008. Now he is serving as a professor at the Nanjing University of Information Science and Technology. His research interests include security of cloud, blockchain and medicine AI.

This workshop addresses the critical challenge of high-dimensional data by focusing on advanced Metaheuristic Optimization techniques for Feature Engineering. Feature selection and reduction are essential steps in improving the efficiency, accuracy, and interpretability of AI models across all domains. This session aims to unite researchers exploring novel algorithms and hybrid methods derived from swarm intelligence, evolutionary computation, and physics-based models to tackle complex data challenges. Contributions are sought on both theoretical breakthroughs and robust real-world applications in critical sectors.

 

Potential topics include:

• Novel Swarm Intelligence and Evolutionary Algorithms for Feature Selection.
• Hybrid and Ensemble Models for feature selection and feature reduction.
• Multi-Objective Optimization techniques for balancing feature count and model accuracy.
• Feature Selection and Engineering in High-Dimensional and Big Data environments.
• Feature Selection in Medical and Healthcare applications (e.g., diagnostics, image analysis).
• Feature Selection in Industrial and Manufacturing applications (e.g., fault diagnosis).
• Feature Selection in Agriculture and Environmental Sensing applications.
• Dimensionality Reduction and Feature Extraction techniques using optimization.
• Techniques for Interpretable Feature Selection (XAI).

 

Qusay Shihab Hamad University of Information Technology and Communications (UOITC), Baghdad, Iraq

Dr. Qusay Shihab Hamad is a Lecturer at the University of Information Technology and Communications (UOITC), Baghdad, Iraq. He holds a Ph.D. in Computational Intelligence from Universiti Sains Malaysia (USM), graduating On Time (GOT) and receiving the Sanggar Sanjung Award for the Best Journal Publication of the Year 2022.

His primary research interests lie at the intersection of Metaheuristic Optimization Algorithms (such as Swarm Intelligence), Deep Learning, and Feature Selection, with a focus on high-dimensional data analytics and smart healthcare applications (e.g., medical image diagnostics).

Dr. Hamad is an active contributor to the academic community, serving on the Technical Program Committees (TPC) for numerous international conferences, including IEEE CyberC 2025, ICCAIS (2025 & 2024), ITSA 2025, and ITPES 2025. He is also recognized for his expertise in evaluating institutional impact, having served as an evaluator for WURI (The World University Ranking for Innovation).

AI is integrated into scientific discovery ever more profusely to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. The main goal of this workshop is to discover synergy across a variety of scientific fields, encourage interdisciplinary discussions, and enhance the flow of knowledge between AI and Science communities. In the current AI era, successes of AI methods in different fields of science have alluded to the general effectiveness of collecting large simulated data, finding suitable architectures, enforcing invariances/equivariances, and utilizing foundation models.

Through our proposed AI for Science workshop, we will bring together experimentalists, domain scientists, and ML researchers to discuss where this boundary lies. Our workshop will highlight common bottlenecks in developing AI methods across scientific application domains, and delve into solutions that can unlock progress across all of these domains. We welcome submissions from all AI for Science areas, but we concentrate our talks and panel on the reach and limits of AI for scientific discovery.

 

Potential topics include:

• Multi-domain scientific reasoning
• High-fidelity generative & surrogate simulators
• Experimental data scarcity bias
• Design and development of agentic AI systems for scientific discovery
• Theoretical foundation for scientific agentic AI
• Practical application of scientific agentic AI
• AI for Climate Science and Environmental Modeling
• AI for Environmental Science
• AI for Healthy Aging and Longevity
• Agentic AI in Financial Services
• AI in Agriculture (AgriAI)
• AI for Urban Planning
• AI for Transportation
• AI for Education
• AI for CyberSecurity
• AI governance
• Federated Learning for Critical Applications
• Ethics and Reproducibility

 

Wei Fang Nanjing University of Information Science & Technology, China

Dr. Wei Fang is currently a Professor and Doctoral Supervisor at the Nanjing University of Information Science and Technology in China, also with State Key Laboratory for Novel Software Technology, Nanjing University. He is a Visiting Senior Research Fellow at King's College London. He is also a distinguished member of China Computer Federation, and executive committee member of Computer Application Committee of China Computer Federation. He has published 70+ papers in conferences and journals, including 40+ SCI indexed papers, obtained 15 national invention patents, and won more than 40 honors.

His research interests cover Artificial Intelligence for Science, Big Data in Meteorology, Machine Learning, and Computer Vision, etc.

Prof. Dr. Wei Fang is an active contributor to the academic community, serving on the Technical Program Committees (TPC) or Publication Chairs for numerous international conferences, including ACM TURC_AIS 2019, IEEE MLAI 2022, CCVPR2023&2024,SIGPRO 2025, CCRIS 2026, DIPCA 2026, ECNET 2026 and ICDIP 2026, etc. He is also recognized for his expertise in evaluating institutional impact, having served as a guest editor for several SCI journals.

Yijing Li King’s college London , UK

Dr Yijing Li is currently a Senior Lecturer in Urban Informatics at King’s college London, the Programme Director for MSc program in Urban Informatics (MSc UI) and used to be the acting Director of Centre for Urban Studies in 2024/25.Prior to joining King’s, Dr Li was teaching at University of Warwick on digital cities and was a Lecturer on Emgergency Management at China Executive Leadership Academy Pudong (CELAP). She is in lead of over 40 data-driven projects in urban context on safety, health, sustainability and disaster responses, in close partnerships with public sectors and industries in London, with a focus on multimodal geospatial data mining and linkage, multi-disciplinary methods integration and telling stories with data-generated insights for policy impacts and real-world problem solving. She holds a PhD degree in Geography of Crime from University of Cambridge, and a MSc degree in Urban Ecology from Peking University, China.

Amin Karami University of East London, UK

Dr. Amin Karami completed his PhD in Computer Architecture in 2015 at Universitat Politècnica de Catalunya Barcelona Tech (UPC) and is currently an Associate Professor in AI and PG Academic Lead at the University of East London (UEL), UK. His research focuses on Big Data Technologies and Computational Intelligence. He secured £1.2m in funding to promote AI and Data Science among under-represented groups. This initiative aims to help both far-STEM and non-STEM students become skilled AI and data analysts. Dr. Karami also serves as an external examiner and adviser for various UK institutions and companies, enhancing AI skills among apprentices and computing experts.