Assist. Prof. Dr. Mustaqeem Khan | Artificial intelligence | Best Researcher Award

Assist. Prof. Dr. Mustaqeem Khan | Assistant Professor | United Arab Emirates University | United Arab Emirates

Academic Background

Dr. Mustaqeem Khan is a distinguished researcher and academic in the field of Artificial Intelligence and Signal Processing. He earned his Doctorate in Software Convergence from Sejong University, South Korea, where his research focused on emotion recognition using deep learning. He also holds a Master’s degree in Computer Science from Islamia College Peshawar, Pakistan, where he was awarded a Gold Medal for academic excellence, and a Bachelor’s degree in Computer Science from the University of Agriculture, Peshawar. Dr. Khan’s scholarly impact is reflected in his remarkable research record, with Scopus indexing 47 documents and over 2,412 citations, resulting in an h-index of 20. On Google Scholar, his work has gained over 2,934 citations, maintaining an h-index of 21 and an i10-index of 31, positioning him among the top two percentage scientists globally.

Research Focus

His research primarily explores Speech and Audio Signal Processing, Emotion Recognition, and Deep Learning. Dr. Khan’s studies integrate multi-modal data analysis through advanced architectures, such as CNNs and Transformers, for applications in speech emotion recognition, computer vision, and energy analytics.

Work Experience

Dr. Khan serves as an Assistant Professor at the United Arab Emirates University, contributing to teaching, research supervision, and curriculum development. Previously, he worked as a Postdoctoral Fellow and Lab Coordinator at the Mohamed Bin Zayed University of Artificial Intelligence, where he collaborated with the Technical Innovation Institute on drone detection systems and managed multidisciplinary AI research teams. Before that, he gained substantial academic and research experience as a Research Assistant at Sejong University and as a Lecturer at Islamia College Peshawar, mentoring students in core computer science and artificial intelligence subjects.

Key Contributions

Dr. Khan has developed several advanced deep learning models, including hybrid attention transformers, multimodal cross-attention networks, and ensemble architectures for audio-visual recognition tasks. His work has contributed to advancements in emotion recognition, drone-based surveillance, and smart city analytics. He has also participated in major funded projects supported by the National Research Foundation of Korea and the Technology Innovation Institute, UAE.

Awards & Recognition

He has been honored with multiple distinctions, including Best Paper Awards, an Outstanding Research Award during his Ph.D., and recognition as a Gold Medalist for academic performance. His inclusion among the Top 2% Scientists (2023–2024) underscores his exceptional research influence and scholarly excellence.

Professional Roles & Memberships

Dr. Khan is an editorial board member and associate editor for several international journals, including the Annals of Applied Sciences and the European Journal of Mathematical Analysis. He serves as a reviewer for over 35 prestigious journals such as IEEE Access, Applied Soft Computing, and Knowledge-Based Systems, actively contributing to academic quality and peer review.

Profile

Scopus | Google Scholar | ORCID

Featured Publications

Khan, M., Ahmad, J., El Saddik, A., & Gueaieb, W. (2025). Joint Multi-Scale Multimodal Transformer for Emotion Using Consumer Devices. IEEE Transactions on Consumer Electronics.

Khan, M., Tran, P. N., Pham, N. T., & Othmani, A. (2025). MemoCMT: Multimodal Emotion Recognition Using Cross-Modal Transformer-Based Feature Fusion. Nature Scientific Reports.

Khan, M., Ahmad, J., El Saddik, A., & Gueaieb, W. (2024). Drone-HAT: Hybrid Attention Transformer for Complex Action Recognition in Drone Surveillance Videos. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Khan, M., Kwon, S. (2021). Optimal Feature Selection Based Speech Emotion Recognition Using Two-Stream Deep Convolutional Neural Network. International Journal of Intelligent Systems.

Khan, M., Kwon, S. (2021). Att-Net: Enhanced Emotion Recognition System Using Lightweight Self-Attention Module. Applied Soft Computing.

Impact Statement / Vision

Dr. Mustaqeem Khan envisions advancing AI systems capable of understanding human emotions and behaviors with precision and empathy. His goal is to integrate deep learning and multimodal intelligence into real-world applications that enhance human–machine interaction, healthcare, and smart technologies. His ongoing commitment to innovation continues to shape the future of intelligent computing and global research collaboration.

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 📖