Dr. Ling Mei | Deep Learning Network | Best Researcher Award

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Dr. Ling Mei | Deep Learning Network | Best Researcher Award

Dr. Ling Mei, Wuhan University of Science and Technology, China

Dr. Ling Mei is a distinguished researcher specializing in artificial intelligence, computer vision, and deep learning networks. He holds a Ph.D. in Engineering from Sun Yat-sen University, one of China’s top institutions, and completed a visiting scholar program at the University of British Columbia (UBC), Department of Computer Science, through the National Outstanding Young Researchers Program. Dr. Mei is a tenured faculty member and master’s supervisor, with a prolific research portfolio including 16 SCI/EI journal papers, 7 SCI articles, 3 granted national invention patents, and a software copyright. His innovative LSN-GTDA framework integrates pedestrian movement analysis for urban planning and public safety, emphasizing multimodal uncertainty in trajectory prediction. Recognized as a Provincial Research Talent of China in 2024, Dr. Mei’s groundbreaking contributions position him as a leader in AI-driven solutions for societal challenges. 🌟📊🤖

Professional Profile

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Suitability for Award

Dr. Ling Mei’s exceptional contributions to artificial intelligence and computer vision make him an ideal candidate for the Best Researcher Award. His groundbreaking LSN-GTDA framework addresses multimodal uncertainty in pedestrian trajectory prediction, significantly advancing urban planning and public safety strategies. By leveraging symmetrical U-Net networks and a novel thermal diffusion process, Dr. Mei has enhanced uncertainty management and interpretability in AI applications. With 16 SCI/EI journal publications, 7 SCI articles, and multiple national invention patents, his research has had a profound impact on academia and industry. Dr. Mei’s recognition as a Provincial Research Talent of China in 2024 underscores his leadership in the field. His innovative solutions to complex societal challenges demonstrate a deep commitment to advancing AI technologies and their real-world applications. 🏆🤖🌍

Education

Dr. Ling Mei has an exemplary academic background, earning a Ph.D. in Engineering from Sun Yat-sen University in 2021, a top 10 university in China. He further enriched his expertise through a prestigious visiting scholar program at the University of British Columbia (UBC), Department of Computer Science, funded by the National Outstanding Young Researchers Program. During this program, Dr. Mei engaged in cutting-edge research on AI and computer vision, collaborating with global experts. His advanced education has equipped him with a robust foundation in artificial intelligence, deep learning networks, and computer vision, enabling him to address complex challenges in urban planning and public safety. Dr. Mei’s commitment to academic excellence and innovative research highlights his potential to drive advancements in AI-driven technologies. 🎓🤖📚

Experience 

Dr. Ling Mei serves as a tenured faculty member and master’s supervisor, where he mentors the next generation of researchers in artificial intelligence and computer vision. His academic career is complemented by a year-long visiting scholar program at the University of British Columbia (UBC), where he contributed to advanced AI research. Dr. Mei has an impressive record of 16 SCI/EI journal publications, including 7 SCI articles, and holds 3 granted national invention patents, with 3 more patents under review. His innovative research focuses on pedestrian movement analysis and multimodal trajectory prediction, which have practical applications in urban planning and public safety. Dr. Mei’s professional journey reflects his dedication to leveraging AI for societal impact and fostering interdisciplinary collaboration. 🌟📊🔬

Awards and Honors 

Dr. Ling Mei’s outstanding contributions to AI and computer vision have earned him prestigious accolades, including recognition as a Provincial Research Talent of China in 2024. This honor highlights his leadership and innovation in addressing complex societal challenges through AI-driven solutions. Dr. Mei was selected for the National Outstanding Young Researchers Program, enabling him to complete a visiting scholar program at the University of British Columbia (UBC), a testament to his exceptional research capabilities. His achievements include 16 SCI/EI journal publications, 7 SCI articles, 3 granted national invention patents, and 1 software copyright, showcasing his commitment to advancing AI technologies. These accolades underscore Dr. Mei’s role as a pioneering researcher making significant contributions to academia and industry. 🏅🤖📈

Research Focus

Dr. Ling Mei’s research focuses on advancing artificial intelligence in networking, computer vision, and deep learning networks. His innovative LSN-GTDA framework integrates behavioral and stochastic factors to address multimodal uncertainty in pedestrian trajectory prediction, enhancing urban planning and public safety strategies. Dr. Mei employs symmetrical U-Net networks and a novel thermal diffusion process based on signal and system theory to improve uncertainty management and interpretability. His work bridges the gap between theoretical advancements and practical applications, emphasizing the role of AI in solving real-world challenges. Dr. Mei’s research aims to develop robust, scalable solutions that integrate AI-driven insights into societal systems, ensuring a safer and more efficient future. 🌐🤖📊

Publication Top Notes

  • Illumination-invariance Optical Flow Estimation Using Weighted Regularization Transform
    • Citations: 29
    • Year: 2019
  • More Quickly-RRT: Improved Quick Rapidly-Exploring Random Tree Star Algorithm Based on Optimized Sampling Point with Better Initial Solution and Convergence Rate*
    • Citations: 14
    • Year: 2024
  • From Pedestrian to Group Retrieval via Siamese Network and Correlation
    • Citations: 13
    • Year: 2020
  • Deep Representations Based on Sparse Auto-Encoder Networks for Face Spoofing Detection
    • Citations: 13
    • Year: 2016
  • WLD-TOP Based Algorithm Against Face Spoofing Attacks
    • Citations: 13
    • Year: 2015

 

Assist Prof Dr. Huiyun Zhang | Deep learn Awards | Best Researcher Award

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Assist Prof Dr. Huiyun Zhang | Deep learn Awards | Best Researcher Award

Assist Prof Dr. Huiyun Zhang, Henan University, China

Dr. Huiyun Zhang holds an M.S. and Ph.D. in Computer Application Technology and Pattern Recognition and Intelligence Systems, respectively, from Qinghai Normal University. She is currently an Assistant Professor at the School of Software, Henan University, China. Dr. Zhang’s research focuses on deep learning and speech emotion recognition (SER), where she has developed advanced models like MA-CapsNet-DA and CENN, integrating capsule networks, attention mechanisms, and Bi-LSTM to enhance SER accuracy. Her previous role as a research assistant at Baylor University provided valuable interdisciplinary experience. With over 20 publications in top-tier journals, Dr. Zhang has made significant contributions to the field, addressing challenges such as overfitting and model robustness. Her work, combined with her commitment to mentoring and interdisciplinary collaboration, underscores her impactful role in advancing both research and education.

Professional Profile:

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Suitability for the Award

  1. Innovative Research:
    • Dr. Zhang’s development of advanced deep learning models for SER represents significant innovation. Her work on MA-CapsNet-DA and CENN addresses critical challenges in speech emotion recognition, enhancing the accuracy and robustness of these systems.
  2. Research Impact:
    • Her publications in reputable journals and conferences reflect her substantial contributions to the field of deep learning and SER. Her research has practical implications for emotion recognition technology, which is increasingly relevant in various applications.
  3. Leadership and Collaboration:
    • Her experience as an Assistant Professor and her role in interdisciplinary research collaborations underscore her leadership and influence in the field. Her work at Baylor University and Henan University demonstrates her commitment to advancing research and fostering academic growth.
  4. Educational Contributions:
    • Dr. Zhang’s involvement in mentoring and educational projects highlights her dedication to advancing knowledge and supporting the next generation of researchers in artificial intelligence and machine learning.

Summary of Qualifications

  1. Educational Background:

    • M.S. in Computer Application Technology (Qinghai Normal University, 2020).
    • Ph.D. in Pattern Recognition and Intelligence Systems (Qinghai Normal University, 2024).
    • Her educational background reflects a strong foundation in both technology and computer science, culminating in advanced research in pattern recognition and intelligence systems.

  2. Professional Experience:

    • Assistant Professor, School of Software, Henan University, China.
    • Research Assistant, Data Science and Artificial Intelligence Program, Baylor University, USA (one year).
    • Her current role as an Assistant Professor involves advancing research in deep learning and speech emotion recognition (SER). Her previous research assistantship at Baylor University provided valuable interdisciplinary experience.

  3. Research Focus and Contributions:

    • Dr. Zhang’s research is centered on speech emotion recognition (SER), deep learning, and data science. She has developed innovative models such as MA-CapsNet-DA and CENN, which integrate capsule networks, attention mechanisms, and Bi-LSTM to enhance SER accuracy.
    • Her work addresses challenges such as overfitting and model robustness, contributing novel metrics and techniques to improve SER systems.
    • Published over 20 papers in top-tier journals including Expert Systems with Applications and Knowledge-Based Systems, reflecting her significant impact in her field.

  4. Contributions to Research and Development:

    • Dr. Zhang’s innovations in deep learning architectures for SER, including capsule networks and attention mechanisms, are cutting-edge contributions that advance the field.
    • Her role as a visiting scholar and collaboration with Baylor University have broadened her research perspectives and fostered interdisciplinary projects.

Publication Top Notes:

“An Improved Capsule Network for Speech Emotion Recognition” (2022), a book chapter in Communications in Computer and Information Science.

“Research on Speech Emotion Recognition Method Based A-CapsNet” (2022), published in Applied Sciences.

“Attention-Based Convolution Skip Bidirectional Long Short-Term Memory Network for Speech Emotion Recognition” (2021), published in IEEE Access.

These publications demonstrate her advanced research in SER and deep learning models, with notable contributions to improving recognition accuracy and model performance.

Conclusion

Assistant Prof. Dr. Huiyun Zhang is highly suitable for the Best Researcher Award due to her significant contributions to speech emotion recognition and deep learning. Her innovative research, extensive publication record, and active role in academic and community engagement demonstrate her excellence and impact in her field. Dr. Zhang’s work not only advances theoretical understanding but also addresses practical challenges in emotion recognition technology, making her an outstanding candidate for this prestigious award.