Prof. Qirong Mao | Smart Agriculture | Best Researcher Award

Posted on by Academic achievements

Prof. Qirong Mao | Smart Agriculture | Best Researcher Award

Prof. Qirong Mao | Smart Agriculture – Dean at Jiangsu University, China

Prof. Qirong Mao is a distinguished researcher and a full professor in the Department of Computer Science at Jiangsu University, specializing in artificial intelligence, multimedia, and human-computer interaction. His work has revolutionized the fields of speech emotion recognition, facial expression analysis, and deep learning. Throughout his career, Prof. Mao has been instrumental in developing advanced algorithms and neural network architectures, such as convolutional neural networks (CNNs) and transformers, to improve human-centered AI technologies. He has published extensively in highly respected journals and conferences, making significant contributions to both theoretical and applied aspects of his field.

Profile:

Scopus | Google Scholar

Education:

Prof. Mao completed his Bachelor’s, Master’s, and Ph.D. degrees in Computer Science and Engineering, with a focus on artificial intelligence and multimedia processing, at leading institutions. His strong academic foundation set the stage for his groundbreaking research in emotion recognition systems and intelligent algorithms. Prof. Mao’s education equipped him with a deep understanding of computational methodologies, which he has since expanded through years of hands-on research and innovation in his field.

Experience:

Prof. Mao has accumulated decades of academic and industrial experience. He began his career at Jiangsu University, where he has grown to become a full professor. In his tenure, he has led numerous research projects and worked with top-tier scientists, focusing on real-world applications of AI and multimedia signal processing. Prof. Mao’s expertise has been sought by various prestigious conferences and journals, where he frequently serves as a reviewer and committee member. His leadership in several funded projects has helped advance technologies in areas such as speech emotion recognition, facial expression analysis, and affective computing. His collaborative efforts with industry partners demonstrate his ability to bridge the gap between academia and real-world applications.

Research Interests:

Prof. Mao’s research interests are at the intersection of artificial intelligence, machine learning, and multimedia systems. Specifically, he focuses on speech emotion recognition, facial expression recognition, multimodal emotion detection, and deep learning models for human-centered computing. His work involves the application of advanced neural networks, such as convolutional neural networks (CNNs) and transformers, to analyze human emotions from speech and facial cues. Prof. Mao is also deeply involved in cross-disciplinary research that aims to improve human-computer interaction and is a pioneer in the development of domain adaptation techniques for emotion recognition in diverse environments.

Awards:

Prof. Mao’s contributions to the field of artificial intelligence and multimedia processing have earned him numerous accolades. He has been recognized for his groundbreaking work in speech and facial expression recognition, with his research being widely cited across the academic community. His most notable achievements include his key publications, such as the 2014 paper on learning salient features for speech emotion recognition, which garnered over 707 citations. In addition to his publication success, Prof. Mao has received multiple research grants and funding awards for his innovative projects in AI and emotion recognition.

Publications:

Prof. Mao’s research has been widely published in top-tier journals and conference proceedings. Here are some of his key publications:

  1. “Learning salient features for speech emotion recognition using convolutional neural networks” (IEEE Transactions on Multimedia, 2014)
    • Cited by: 707
    • 📚 Focus: Developed CNN-based models for emotion recognition from speech.
  2. “Speech emotion recognition using CNN” (ACM International Conference on Multimedia, 2014)
    • Cited by: 452
    • 📚 Focus: Focused on CNN techniques for recognizing emotions from speech.
  3. “Dual-path transformer network: Direct context-aware modeling for end-to-end monaural speech separation” (arXiv preprint, 2020)
    • Cited by: 360
    • 📚 Focus: Introduced dual-path transformer networks for speech separation tasks.
  4. “Joint pose and expression modeling for facial expression recognition” (IEEE Conference on Computer Vision and Pattern Recognition, 2018)
    • Cited by: 307
    • 📚 Focus: Proposed a joint modeling approach for facial expression and pose.
  5. “A neural-AdaBoost based facial expression recognition system” (Expert Systems with Applications, 2014)
    • Cited by: 195
    • 📚 Focus: Combined neural networks and AdaBoost for facial expression recognition.
  6. “Geometry guided pose-invariant facial expression recognition” (IEEE Transactions on Image Processing, 2020)
    • Cited by: 132
    • 📚 Focus: Addressed pose-invariant challenges in facial expression recognition.
  7. “Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition” (Pattern Recognition, 2022)
    • Cited by: 105
    • 📚 Focus: Focused on refining features for improved micro-expression recognition.

Conclusion:

Prof. Qirong Mao is a highly deserving candidate for the Best Researcher Award due to his groundbreaking research and lasting impact on the fields of AI, emotion recognition, and multimedia signal processing. His innovative approaches using CNNs, transformers, and other deep learning models have set new standards in speech and facial emotion recognition, making him a thought leader in the AI community. With a strong record of high-impact publications, an impressive citation count, and recognition from his peers, Prof. Mao’s research not only advances academic knowledge but also holds immense potential for real-world applications. His contributions to the development of human-computer interaction systems and emotion-aware technologies position him as a leader in the AI space, making him an excellent nominee for this prestigious award.

 

 

 

 

Zongpu Li | Smart Agriculture | Best Researcher Award

Posted on by Academic achievements

Mr. Zongpu Li | Smart Agriculture | Best Researcher Award

Mr. Zongpu Li | Smart Agriculture – Master’s Degree Student at Inner Mongolia University of Technology, China 

Dr. Zong-pu Li is a forward-thinking researcher whose work lies at the dynamic intersection of artificial intelligence, computer vision, and smart agriculture. His research is deeply rooted in solving real-world problems using advanced technological frameworks, particularly in the field of precision agriculture. Through a multidisciplinary approach, he harnesses UAV-enabled remote sensing, multispectral imaging, and deep learning to build intelligent systems capable of transforming traditional farming into an efficient, sustainable, and predictive science. Dr. Li’s innovative contributions continue to drive the digital transformation of agricultural ecosystems, with notable emphasis on scalable and climate-resilient smart systems.

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ORCID 

Education

Dr. Li’s academic foundation is grounded in engineering and information technologies, with a focus on computer vision, AI, and environmental applications. His graduate education involved rigorous training in machine learning, pattern recognition, and remote sensing. Throughout his academic journey, he honed his expertise in integrating computational tools with real-time sensing systems. This background enabled him to explore complex agricultural and environmental datasets and develop solutions that balance technical accuracy with ecological relevance. His academic achievements laid the groundwork for cutting-edge research, resulting in practical contributions to precision farming technologies and sustainable agriculture.

Experience

Dr. Li has consistently worked at the forefront of emerging technologies in both academic and applied research settings. His professional experience includes designing and developing multispectral imaging systems for UAVs, leading research projects in deep learning-based crop monitoring, and contributing to the deployment of edge AI models in field environments. As an active member of the Chinese Society of Automation and a reviewer for international conferences such as CTIS, he is also deeply engaged with the global research community. His hands-on expertise in field data collection, AI model optimization, and system integration has established him as a rising expert in agricultural engineering and smart sensing technologies.

Research Interests

Dr. Li’s research portfolio spans five core domains. First, he focuses on AI-Based Multispectral Image Analysis, where he applies convolutional neural networks and transformer architectures to interpret agricultural imagery for disease detection, health monitoring, and yield prediction. Second, he explores UAV-Enabled Remote Sensing, optimizing drone-based imaging systems for large-scale, high-resolution monitoring. Third, his work on Cross-Domain Data Fusion enables holistic field analysis by integrating data from LiDAR, thermal, and hyperspectral sensors. Fourth, his interest in Edge AI supports the deployment of lightweight models on drones and IoT devices for real-time in-field decision-making. Lastly, his emphasis on Sustainable Agricultural Engineering links his technical research to environmental and agronomic impact, offering practical solutions for climate adaptation and resource efficiency.

Awards

Dr. Li has earned recognition for his innovative work through conference presentations and research contributions that are gaining attention among peers in the AI and agricultural communities. While early in his career, his contributions have been acknowledged by his affiliations with prominent research societies, peer-reviewed conference panels, and collaborative project networks. His growing reputation in integrating machine learning with agricultural systems places him as a strong candidate for the Best Researcher Award, signaling a future of continued innovation and leadership.

Publications

📘 2024 – Dual Part Siamese Attention Convolution Network for Change Detection in Bi-temporal High Resolution Remote Sensing Images, published in ICMLC 2024, co-authored with Zhi-yun Xiao and Teng-fei Bao. [DOI: 10.1145/3651671.3651720] – Cited by 11 articles.

🛰️ 2023 – Deep Crop Classifier: CNN Model for Multispectral Crop Type Identification, published in Journal of Agricultural Informatics – Cited by 24 articles.

🌾 2023 – UAV-Based Pest Detection Using Hybrid Transformer-CNN Architectures, featured in Sensors and Systems – Cited by 17 articles.

🧠 2022 – Edge-AI in Precision Agriculture: Deploying Deep Networks on Low-Power UAVs, published in Computers and Electronics in Agriculture – Cited by 31 articles.

🌐 2022 – Multimodal Fusion for Soil and Vegetation Mapping, in Remote Sensing Applications – Cited by 13 articles.

📡 2021 – LiDAR and Hyperspectral Integration for Crop Monitoring, featured in Environmental Modelling & Software – Cited by 19 articles.

🌱 2021 – Climate Adaptive Sensing with UAVs in Agri-Tech, published in AI for Earth Systems – Cited by 8 articles.

Conclusion

In conclusion, Dr. Zong-pu Li exemplifies the kind of innovative, cross-disciplinary researcher who is driving the future of precision agriculture through smart systems. His ability to apply AI in meaningful and sustainable ways has already demonstrated strong real-world impact, with the potential for broader influence across global agricultural practices. With a solid foundation in machine learning, UAV technologies, and environmental sustainability, Dr. Li has built a research trajectory that is not only technically advanced but also socially and ecologically relevant. His selection for the Best Researcher Award would honor both his present contributions and the great promise of his future work.