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.

Digital transformation has accelerated the evolution of Industry 4.0/5.0 and smart cities, emphasizing human-machine collaboration, green sustainability, real-time responsiveness, and urban-industrial synergy. However, these paradigms face unprecedented challenges: heterogeneous data streams from IoT devices, uncertain operational environments in industrial production and urban management, and the demand for low-latency intelligent decision-making.

AI-driven soft computing (e.g., fuzzy logic, evolutionary algorithms) and edge intelligence have emerged as core enablers to address these pain points. Soft computing mimics human reasoning to handle ambiguity and complexity, while edge intelligence brings AI capabilities closer to data sources, ensuring efficient real-time processing. Their integration empowers Industry 4.0/5.0 with adaptive optimization, predictive maintenance, and green manufacturing, and equips smart cities with intelligent traffic management, energy-efficient grids, and resilient public services—directly aligning with the United Nations Sustainable Development Goal 9 (SDG 9: Industry, Innovation, and Infrastructure).

This Workshop aims to gather cutting-edge research on the fusion of AI, soft computing, and edge intelligence, exploring their innovative applications in Industry 4.0/5.0 and smart urban ecosystems. It will serve as a platform to showcase interdisciplinary advances, bridge theory and practice, and promote academic-industrial collaboration—leveraging the expertise of four guest editors with profound insights in related fields.

Potential topics include:

• AI-soft computing hybrid models (e.g., fuzzy neural networks, evolutionary algorithm-optimized edge AI)
• Edge intelligence optimization for resource-constrained environments (e.g., lightweight AI/soft computing algorithms)
• Distributed AI and federated learning for edge-Cloud synergy in industrial/urban systems
• Trustable and interpretable AI-driven soft computing (e.g., privacy-preserving fuzzy data aggregation, XAI for edge decisions)
• Fuzzy logic-based predictive maintenance and fault diagnosis for cyber-physical systems (CPS)
• AI-driven green optimization for Industry 5.0 (e.g., energy-saving, carbon emission reduction via soft computing)
• Human-machine collaborative decision-making using soft computing (e.g., fuzzy cognitive maps for flexible production lines)
• Digital twin-enabled edge intelligence for industrial process control and optimization
• Edge AI and soft computing for intelligent traffic management (e.g., dynamic signal optimization, vehicle/pedestrian detection)
• IoT sensor data analysis via AI-soft computing fusion (e.g., environmental monitoring, noise reduction in urban data)
• Smart grid operation and renewable energy management (e.g., fuzzy control for distributed energy resource balancing)
• Edge-enabled smart building, waste management, and public safety systems
• Urban-industrial synergy via AI-driven edge-soft computing (e.g., industrial pollution control for urban environments)
• Large-scale deployment cases of edge intelligence + soft computing in Industry 4.0/5.0 or smart cities
• Performance evaluation frameworks for AI-soft computing-edge integration systems

Jian Su Zhejiang Sci-tech University, China

Dr. Jian Su (Member, IEEE) received the B.S. degree in electronic and information engineering from Hankou University, the M.S. degree in electronic circuit and systems from Central China Normal University, and the Ph.D. degree (Hons.) in communication and information systems from the University of Electronic Science and Technology of China (UESTC) in 2016. He has been an Associate Professor with the School of Software, Nanjing University of Information Science and Technology, since 2017. His current research interests include the Artificial Intelligence, Internet of Things, RFID, and Distributed Computing. He is a member of ACM. He is an Associate Editor of the IEEE Journal of Radio Frequency Identification, an Associate Editor of CMC-Computers Materials & Continua, and the Editor-in-Chief of the Journal on Internet of Things. He also serves as the General Chair of the International Conference on Computer Engineering and Networks.

Zilong Jin Zhejiang Sci-tech University, China

Prof. Zilong Jin (Member, IEEE) received the B.E. degree in computer engineering from Harbin University of Science and Technology, China, in 2009, and the M.S. and Ph.D. degrees in computer engineering from Kyung Hee University, Korea, in 2011 and 2016, respectively. He is currently a professor at School of Information Science and Engineering at Zhejiang Sci-Tech University, China. His research interests include mobile wireless networks, IoT security, and mobile edge networks. He is an editor of the KSII Transactions on Internet and Information Systems.

With the rapid advancement of Internet of Things (IoT) and mobile communication technologies, intelligent transportation systems (ITS) are transitioning to integrated Vehicle-Road-Cloud frameworks. This evolution harnesses state-of-the-art perception, communication, computation, and control technologies to enable real-time, comprehensive, and end-to-end intelligent traffic management. The primary emphasis is on boosting traffic efficiency while prioritizing safety in intricate road scenarios.

This workshop serves as a vital forum for researchers and practitioners to exchange cutting-edge innovations and address key challenges in intelligent connected vehicles and cooperative driving. We invite submissions on pioneering methods for enhancing traffic safety, real-time vehicle-road collaboration, autonomous driving, and cloud-based intelligent transportation services.

Topics of interest include, but are not limited to:

• High-precision positioning and perception in V2X systems;
• Safety evaluation and risk alert mechanisms;
• Data fusion and resource optimization in vehicle-road-cloud architectures;
• AI and machine learning applications for traffic management;
• Interaction strategies in mixed traffic environments (e.g., human-AV coordination);
• Conflict resolution and behavior prediction for heterogeneous vehicle fleets;
• Proactive collision avoidance and vulnerable road user protection in dynamic scenarios.
• Cyber-physical systems and digital twins for real-time traffic state assessment and prediction.
• Edge and cloud computing solutions for intelligent transportation services.
• Ultimately, the workshop seeks to foster synergy between smart vehicles and infrastructure, advancing a safer, more efficient, and sustainable mobility ecosystem.

Keywords: Vehicle-Road-Cloud Integration, Smart Transportation, Traffic Safety, V2X, Autonomous Driving, Intelligent Connected Vehicles

Zhongbin Luo Chongqing Jiaotong University, China

Zhongbin Luo is a Professor-level Senior Engineer at the Digital Transportation and Smart City Research Institute of China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd., and a Master's Supervisor at Chongqing Jiaotong University and Shijiazhuang Tiedao University. His core research interests lie in traffic safety, intelligent transportation, vehicle-road coordination, autonomous driving, and Vehicle-Road-Cloud integration. He is highly active in the academic community, serving as a Member of the Intelligent Transportation Branch of the Shaanxi Highway Society, a Road Traffic Standardization Expert, and an Off-Campus Supervisor for Master's Students. He is a Youth Editorial Board Member for Chain and Urban Roads, Bridges and Flood Control, and a senior Reviewer for over 10 journals including IJTET, IEEE access, PLOS One and Urban Mass Transit Research. He has led sub-projects for one National Key R&D Program of China and one Chongqing Major Special Project. He holds 18 national invention patents, has published 10 journal papers as the first/corresponding author, and published 2 monographs. He has received numerous awards, including a Second Prize for National Excellent Engineering Consulting Achievement and a First Prize for Scientific and Technological Progress from the China Highway and Transportation Society.

Yanqiu Bi Chongqing Jiaotong University, China

Yanqiu Bi is an Associate Professor, Doctoral Supervisor, and Master's Supervisor who has been selected as a Young Top Talent by Chongqing Jiaotong University. He completed his Bachelor's, Master's, and Ph.D. studies at Chang'an University, earning his Doctor of Engineering degree in June 2020. Dr. Bi's primary research interests focus on three areas: multi-scale rheology of asphalt materials and recycling technology for aged asphalt; full-life performance evolution and intelligent monitoring systems for asphalt pavement; and novel energy-saving and carbon-reducing materials and key technologies for asphalt pavement. His significant research achievements include leading 3 national and provincial-level projects, such as the National Natural Science Foundation of China Youth Fund and the Chongqing Natural Science Foundation General Program, and publishing over 30 academic papers indexed by SCI.

Yunze Wang Shijiazhuang Tiedao University, China

WANG Yunze is an Associate Professor and Master's Supervisor with a Ph.D. in Engineering. He serves as the Deputy Director of the Department of Transportation Engineering and is the team leader for the "Strengthen the Nation" special action program for a "Dual-Pioneer" faculty party branch at his university in Hebei Province. His research expertise covers various aspects of intelligent and sustainable transportation, including road traffic safety, intelligent traffic control, vehicle platooning, trajectory planning for unmanned vehicles, ethical standards for autonomous driving, and low-carbon transport. He applies methods such as Game Theory, Machine Learning, Reinforcement Learning, and Multi-Agent Cooperative Control in intelligent transportation systems. Dr. Wang has presided over or participated in 10 provincial/ministerial-level projects, including key R&D projects, Natural Science Foundation projects, and Social Science research projects in Hebei Province, in addition to over 30 industry-funded enterprise projects. He has published over 20 academic papers and holds 8 authorized invention patents. He has received a Third Prize for Teaching Achievement in Hebei Province and has been recognized as an Excellent Teacher and an Advanced Individual for "Three-Commitments Education" at Shijiazhuang Tiedao University. He is also a committee member of the Highway Engineering Department of the World Transport Convention (WTC) and a director of the Shijiazhuang Low-Carbon Society.

This workshop explored the ethical landscape of AI-powered automation in education, focusing on its applications in assessment, learning analytics, and the preservation of academic integrity. As AI systems increasingly support grading automation, plagiarism detection, adaptive learning, and predictive analytics, educators and institutions face complex ethical challenges involving bias management, transparency, data governance, fairness, accountability, and student rights. The workshop also provided practical strategies for building AI-ready institutional policies, strengthening academic integrity protocols, and promoting ethical use of automation across culturally diverse learning environments. Emphasis was placed on education in ASEAN contexts, where AI adoption continues to expand, yet governance frameworks remain emergent. At the end of the workshop, participants gained a deeper understanding of ethical risk mitigation, policy development, and responsible implementation of AI tools that uphold fairness, trust, and human-centered learning.

Potential Scope/Topics:

• Ethical and Responsible AI Integration in Higher Education
• AI-Powered Educational Automation: Opportunities, Risks, and Governance Mechanisms
• Learning Analytics and Ethical Decision-Making in AI-Enabled Classrooms
• Academic Integrity in the Age of Generative AI
• Responsible AI Frameworks for Simulation-Based Learning in Education
• Governing AI-Driven Assessment Systems in Higher Education
• Systematic Review and Bibliometric Mapping of Artificial Intelligence in Education
• Generative AI for Teaching and Research: Ethical, Pedagogical, and Governance Perspectives
• Strengthening Higher Education Governance for AI Adoption and Policy Development
• Data Ethics, Algorithmic Bias, and Fairness in AI-Driven Learning Systems
• AI-Enhanced Quality Assurance and Accreditation: Ethical and Governance Considerations
• Higher Education in the AI Era: Ethical and Pedagogical Transformations

Noelah Mae D. Borbon National University, Philippines (NU Lipa)

Dr. Borbon is a tourism and hospitality educator, researcher, and administrator currently serving as a Faculty Member and Campus Research Coordinator at National University Lipa. She has authored and co-authored numerous Scopus-indexed publications in tourism, hospitality, sustainability, AI in education, and cultural heritage. She is also the Founding President of the United Tourism and Hospitality Professionals (UTHP). Her work focuses on research development, AI in education, community engagement, and capacity-building for tourism professionals. With over a decade of academic experience, she has become a vital faculty member and Research Coordinator at National University Lipa, where she is recognized for her strong commitment to teaching and research excellence. Her research impact continues to grow, with 73 publications, 299 citations, and an H-index of 8 in Google Scholar, alongside contributions to Scopus and Web of Science, reflecting her increasing influence in local and international academic communities.

Alice Lacorte National University, Philippines (NU Lipa)

Dr. Alice Maldonado-Lacorte is the Program Chair of BS Computer Science at National University Lipa and formerly served as Dean of the College of Computing Studies at the University of Cabuyao and FAITH Colleges. She is a published book author and an active researcher whose expertise centers on AI-driven systems, machine learning, green computing, and community-based IT development. One of her notable projects is Bakwitfinder, a mobile application used during the 2020 Taal Volcano eruption to help locate evacuees and evacuation centers. Her research portfolio also includes studies on information systems, disaster management technologies, unmanned aerial vehicles, and smart transportation systems. She has contributed to major initiatives such as the Taal Volcano Island Conservancy Program and UP’s Project eSMART. Dr. Lacorte has presented her work in various local and international conferences, including a notable research presentation at Harvard University in 2017, and currently serves as a technical review committee member for several conferences. She is a co-founder of iBarako, a social enterprise fintech startup, and NUSURI, an AI-driven mobile app for cough spectrum analysis for presumptive TB detection, recognized as a National Finalist in the 2025 Philippine Startup Challenge. Her professional affiliations include CHED RQAT Region IV-A, PACUCOA, PSITE, and ISITE.

Irene Balmes National University, Philippines (NU Lipa)

Dr. Balmes is a dedicated academic professional with extensive teaching and leadership experience spanning over 15 years, currently serving as Full-Time Faculty at National University. Demonstrates expertise in information technology education, research coordination, and curriculum development. Adept at fostering student success through academic advising, innovative teaching methods, and a commitment to creating a supportive learning environment. At National University, she contribute to advancing academic programs, enhancing curriculum offerings, and addressing student needs. Passionate about integrating research into IT and CS education, empowering students with industry-relevant knowledge, and driving continuous program improvement. Actively engages in professional growth and collaboration to uphold the institution's academic standards.

Marjeric Buenafe National University, Philippines (NU Lipa)

Dr. Marjeric L. Buenafe served as Associate Professor II at the School of Accountancy, Business & Management, National University – Lipa (NU Lipa), Philippines. His research interests included management practices, digitalization of business operations, and quantitative methods in business research. He provided expertise as resource speaker and statistics specialist for undergraduate and graduate research across Region IVA, and he held leadership roles in professional education-communities, including election as President of the NCR/Luzon Council of Management Educators & Professionals (COMEPP) for 2023-2024. His commitment to bridging rigorous research methodology with applied business management practice contributed significantly to the scholarship and institutional development at NU Lipa.

Jhon Benedict L. Layoc National University, Philippines (NU Lipa)

Jhon Benedict L. Layoc is an Assistant Professor 4 at the National University, Philippines in the Lipa City campus. He is currently studying Doctor of Philosophy in Education Management at the Polytechnic University of the Philippines and finished his Master of Arts in Filipino from the same university, having previously earned a Bachelor of Secondary Education major in Filipino from Rizal Technological University. With 9 years of academic experience as a faculty and researcher, he served in various schools and universities. Known for his active research in language and culture, educational management, artificial intelligence in education, teaching pedagogy, and social sciences, he has published various papers in international journals, including in Scopus-indexed journals, and served as a research mentor. He also has a monograph in a published book used in the Filipino Subject. Being involved in school activities, he advocates for programs utilizing his National Certificate III in Events Management, and serves as a resource speaker. He is recognized as a Microsoft Innovative Educator Expert and received a Sustainability Education certificate from the World Association of Chefs’ Society. During the pandemic, he responded to the needs of the Schools Division of Mandaluyong City by writing and designing Filipino modules and serving as a teacher broadcaster. He is affiliated with prestigious organizations such as the Royal Institution in Singapore and Academic Development for Global Leadership in Education (ADGLE) Philippines, and is a member of the International Association of Contemporary Researchers and the Editorial Board Member of the Ianna Journal of Interdisciplinary Studies, a Scopus-indexed Q2 journal.

Large Language Models are now used in enterprise software, cloud systems, and public services. These models process sensitive text, source code, and operational data. Their rapid adoption exposes new security gaps in training, prompting, and deployment. Teams face risks from prompt injection, data leakage, model extraction, poisoned datasets, and uncontrolled model outputs. The workshop builds on these risks and provides a clear motivation for focused study. Organizations need practical methods to secure LLM pipelines and to use LLMs to improve security operations. Manual rules fail to keep pace with new attack patterns. LLM-based tools for threat hunting, code review, and log analysis give teams faster and more accurate response options.

Through this workshop on AI Security in the Era of Large Language Models, we bring together security engineers, AI researchers, and system designers to examine the security limits of LLM-driven systems. The workshop highlights common gaps in securing LLM training, prompting, and deployment. It also focuses on practical solutions that support safer model behavior and stronger defensive use cases. We welcome submissions from all areas of AI security, but we center our talks and panel on the risks and defensive potential of LLMs in enterprise and cloud environments.

Potential topics include:

• Security risks in LLM-based applications
• Threats to training pipelines and data quality
• Safety controls for LLM outputs
• Evaluation methods for robust and secure model behavior
• Privacy protection in LLM workflows
• Governance and policy requirements for LLM adoption
• Risk assessment frameworks for AI systems
• Secure integration of LLMs into enterprise software
• Human oversight in LLM-supported decision systems
• Red team testing practices for AI systems
• Defensive applications of LLMs in cybersecurity
• Automated analysis of code, logs, and incidents
• Security monitoring for AI-assisted services
• Lifecycle management for secure AI deployment

Brij B. Gupta Asia University

Brij B. Gupta received the PhD degree from Indian Institute of Technology (IIT) Roorkee, India (One of the oldest technical institutes in Asia). In more than 20years of his professional experience, he published over 500 papers in journals/conferences including 30 books and 10 Patents with over 20,000 citations. He has received numerous national and international awards including Canadian Commonwealth Scholarship (2009), Govt. of Canada, Visvesvaraya Faculty Research Fellowship Award (2017), MeitY, Govt of India, IEEE GCCE outstanding and WIE best paper awards and Best Faculty Award (2018 & 2019), NIT KKR, India respectively. Prof Gupta is also serving as Distinguished Research Scientist with LoginRadius Inc., USAwhich is one of leading cybersecurity companies in the world, especially in the field of customer identity and access management (CIAM). He is also selected in the 2021 and 2020 Stanford University’s ranking of the world’s top 2% scientists. He is also a visiting/adjunct professor with several universities (i.e. Temple University, Macquarie University, Yamaguchi University, University of Murcia, Deakin University, Staffordshire University, Swinburne University of Technology, etc) worldwide. He is also an IEEE Senior Member (2017) and also selected as 2021 Distinguished Lecturer in IEEE CTSoc. Dr Gupta is also serving as Member-in-Large, Board of Governors, IEEE Consumer Technology Society (2022-2024). Prof. Gupta is also leading IJSWIS, IJSSCI and IJCAC, IGI Global, as Editor-in-Chief. Moreover, he is also serving as lead-editor of Book Series with CRC, World Scientific and IET press. He also served as TPC members and organized/special session chairs in ICCE 2021, GCCE 2014-2021 and TPC Chair in 2018 INFOCOM:CCSNAWorkshop, Publicity Co-chair in 2020 ICCCN, etc. Dr Gupta is also serving/served as Associate/Guest Editor of IEEE TII, IEEE TITS, IoT, IEEE Big Data, ASOC, FGCS, etc. At present, Prof. Gupta is working as Director, International Center for AI and Cyber Security Research and Innovations, and Distinguished Professor with the Department of Computer Science and Information Engineering (CSIE), Asia University, Taiwan. His research interests include cyber security, cloud computing, artificial intelligence, intrusion detection, blockchain technologies, cyber physical systems, social media and networking.

Kwok Tai Chui Hong Kong Metropolitan University, China

John Kwok Tai Chui received the B.Eng. degree in Electronic and Communication Engineering –Business Intelligence Minor, with first-class honor, and Ph.D. degree in Electronic Engineering from City University of Hong Kong. He was the recipient of international awards in several IEEE events. For instance, he received the 2nd Prize Award (Postgraduate Category) of 2014 IEEE Region 10 Student Paper Contest, and Best Paper Award in IEEE The International Conference on Consumer Electronics-China, in both 2014 and 2015. He had industry experience as Senior Data Scientist in Internet of Things (IoT) company. He joined the School of Science and Technology at the Hong Kong Metropolitan University as a ResearchAssistant Professor.

Akshat Gaurav Asia University

Akshat Gauravis a Senior IEEE Member and a recognized researcher listed in the Stanford–Elsevier Top 2% Scientists (Single-Year 2024) in Artificial Intelligence and Image Processing. He is currently a cyber security researcher atInternational Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan. His research spans cybersecurity, AI-driven attack detection, phishing prevention, semantic web technologies, and intelligent IoT systems.He has authored an extensive body of research published in leading venues including IEEE Transactions, Elsevier, Springer, IGI Global, and ACM. His work has earned multiple accolades, such as Best Paper Awards at IEEE/ACM Cluster, Cloud and Internet Computing, International AI & IoT conferences, and Best Oral Presentation at GCCE 2025.Mr. Gaurav also plays active leadership roles in the research community. He served as Session Chair at IEEE GLOBECOM 2025 and previously as Session Co-Chair at IEEE GCCE 2022. In addition, he contributes as a Technical Program Committee (TPC) member and reviewer for premier venues, including IEEE INFOCOM, GLOBECOM, ICC, ICCE, ANTS, and several high-impact IEEE Transactions and ACM journals.