QIANG QU | Artificial Intelligence Award | Best Researcher Award

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Prof. QIANG QU | Artificial Intelligence Award | Best Researcher Award

PROFESSOR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Dr. Qiang Qu is a distinguished professor and a leading researcher in blockchain, data intelligence, and decentralized systems. He serves as the Director of the Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Additionally, he holds a professorship at Shenzhen University of Advanced Technology and has previously served as a guest professor at The Chinese University of Hong Kong (Shenzhen). Dr. Qu has also contributed as the Director and Chief Scientist of Huawei Blockchain Lab. With a strong international academic presence, he has held research positions at renowned institutions such as ETH Zurich, Carnegie Mellon University, and Nanyang Technological University. His pioneering work focuses on scalable algorithm design, data sense-making, and blockchain technologies, making significant contributions to AI, data systems, and interdisciplinary studies.

Publication Profile

🎓 Education

Dr. Qiang Qu earned his Ph.D. in Computer Science from Aarhus University, Denmark, under the supervision of Prof. Christian S. Jensen. His doctoral research was supported by the prestigious GEOCrowd project under Marie Skłodowska-Curie Actions. He further enriched his academic journey as a Ph.D. exchange student at Carnegie Mellon University, USA. He holds an M.Sc. in Computer Science from Peking University, China, and a B.S. in Management Information Systems from Dalian University of Technology.

💼 Experience

Dr. Qu has a diverse professional background, reflecting his global expertise. Since 2016, he has been a professor at SIAT, leading groundbreaking research in blockchain and distributed IoT security. He also served as Vice Director of Hangzhou Institutes of Advanced Technology (SIAT’s Hangzhou branch). Prior to this, he was an Assistant Professor and the Director of Dainfos Lab at Innopolis University, Russia. His research journey includes being a visiting scientist at ETH Zurich, a visiting scholar at Nanyang Technological University, and a research fellow at Singapore Management University. He also gained industry experience as an engineer at IBM China Research Lab.

🏅 Awards and Honors

Dr. Qu has received several national and international research grants, recognizing his impactful contributions to blockchain and AI-driven data intelligence. He is a prominent editorial board member of the Future Internet Journal and serves as a guest editor for multiple high-impact journals. As an active contributor to the research community, he has been a TPC (Technical Program Committee) member for prestigious conferences and regularly reviews top-tier AI and data systems journals.

🔬 Research Focus

Dr. Qu’s research interests revolve around data intelligence and decentralized systems, with a strong focus on blockchain, scalable algorithm design, and data-driven decision-making. His work has been instrumental in developing efficient data parallel approaches, AI-driven network analysis, and cross-blockchain data migration techniques. His interdisciplinary contributions bridge AI, IoT security, and geospatial analytics, driving innovation in secure and intelligent computing.

🔚 Conclusion

Dr. Qiang Qu stands as a thought leader in blockchain and data intelligence, combining academic excellence with real-world impact. His contributions to AI-driven decentralized systems and scalable data solutions continue to shape the fields of computer science and IoT security. His extensive research collaborations, editorial roles, and international experience make him a key figure in advancing secure and intelligent computing technologies. 🚀

📚 Publications

SNCA: Semi-supervised Node Classification for Evolving Large Attributed Graphs – IEEE Big Data Mining and Analytics (2024). Cited in IEEE 📖

CIC-SIoT: Clean-Slate Information-Centric Software-Defined Content Discovery and Distribution for IoT – IEEE Internet of Things Journal (2024). Cited in IEEE 📖

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing – IEEE Journal on Selected Areas in Communications (2022). Cited in IEEE 📖

On Time-Aware Cross-Blockchain Data Migration– Tsinghua Science and Technology (2024). Cited in Tsinghua University 📖

Few-Shot Relation Extraction With Automatically Generated Prompts – IEEE Transactions on Neural Networks and Learning Systems (2024). Cited in IEEE 📖

Opinion Leader Detection: A Methodological Review – Expert Systems with Applications (2019). Cited in Elsevier 📖

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification– IEEE Transactions on Affective Computing (2021). Cited in IEEE 📖

Efficient Online Summarization of Large-Scale Dynamic Networks –  IEEE Transactions on Knowledge and Data Engineering (2016). Cited in IEEE 📖

slimane arbaoui | Artificial Intelligence | Young Scientist Award

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Mr. slimane arbaoui | Artificial intellegence | Young Scientist Award

Cube-SDC team, INSA Strasbourg, University of Strasbourg , 24 Bd de la Victoire, Strasbourg, 67000, France, insa strasbourg, France

Slimane Arbaoui is a dedicated final-year Computer Science student at École Supérieure en Informatique (ESI) in Sidi Bel Abbess, Algeria, specializing in Android application development and machine learning. 🎓 His skills span Java-based Android development, data integration, and advanced problem-solving in software, alongside a versatile understanding of multiple programming languages, including Python and Kotlin. Slimane has applied his AI knowledge to impactful projects, even authoring a research paper. 📚 Known for his innovation and strong analytical skills, Slimane is passionate about tackling real-world challenges with technology.

Publication Profile

Scopus

Education

Slimane completed his State Engineering and Master’s degrees in Computer Science at ESI SBA in 2023. 🎓 His academic journey has strengthened his technical expertise and provided a foundation in both theoretical and applied computing, with a focus on machine learning, mobile app development, and web technologies.

Experience

During his internship at INSA-Strasbourg, France 🇫🇷, Slimane applied machine learning to improve battery health prediction, developing models that track and identify factors contributing to battery degradation. At CNAS in Algeria, he gained practical insights into network database applications and web app development. 💻 As a freelancer on Upwork, Slimane developed Android applications and managed web back-end services, demonstrating his versatility in real-world projects.

Research Focus

Slimane’s research interests center on artificial intelligence and machine learning, with a special focus on NLP applications, sentiment analysis, and health data prediction. 🧠 His projects include sentiment analysis and fake news detection in Arabic language datasets, alongside health management applications that leverage data-driven insights to enhance service quality. His work in battery health prediction highlights his proficiency in machine learning model development and evaluation.

Awards and Honours

Slimane holds several certifications, including Microsoft Certified: Azure Fundamentals and the Android Basics Nanodegree. 🏅 His achievements in AI include completing courses on deep learning and machine learning through Kaggle and Coursera, which demonstrate his commitment to continuous learning and professional development.

Publication Top Notes

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

SOCXAI: Leveraging CNN and SHAP Analysis for Battery SOC Estimation and Anomaly Detection

Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries