Tinggui Chen | Engineering | Research Excellence Award

Dr. Tinggui Chen | Engineering | Research Excellence Award 

Hefei University of Technology | China

Dr. Tinggui Chen is a highly accomplished researcher and academic in the field of mechanical engineering, with a specialized focus on acoustic metamaterials, phononic crystals, and advanced signal detection techniques. He completed his doctoral studies in mechanical engineering under the supervision of Prof. Dejie Yu at Hunan University, after earning both his bachelor’s degree from Hainan University and master’s degree from Hunan University. During his doctoral tenure, he developed innovative methodologies for enhancing acoustic sensing and signal detection using engineered metamaterials, establishing a strong foundation for his research career. Dr. Chen’s work is characterized by its combination of theoretical insight and experimental rigor, particularly in the design and application of gradient metamaterials, coiling-up structures, and space-time-modulated systems. His research has led to significant advancements in weak signal detection, directional acoustic sensing, and energy amplification in phononic systems. Notably, his studies on multi-frequency signal enhancement via gradient defect phononic crystals and space-time-modulated airborne acoustic circulators demonstrate his ability to bridge fundamental physics with practical engineering applications. He has actively contributed to the international scientific community through his extensive publication record, which includes articles in high-impact journals such as Measurement, Physical Review Applied, IEEE Transactions on Industrial Informatics, Mechanical Systems and Signal Processing, Journal of Sound and Vibration, IEEE Sensors Journal, Journal of Physics D: Applied Physics, and Physical Review B. These publications reflect his sustained focus on acoustic metamaterials, phononic crystal resonators, and novel techniques for signal demodulation and amplification, marking him as a leading expert in his domain. Dr. Chen’s research trajectory has also been enriched by international exposure and collaborative experiences. As a visiting scholar at EPFL under Prof. Romain Fleury, he explored cutting-edge experimental demonstrations in acoustic systems, further strengthening his expertise in wave manipulation and signal processing. Currently, as a postdoctoral researcher at Shanghai Jiao Tong University and an assistant professor at Hefei University of Technology, he continues to advance both fundamental and applied research, integrating computational modeling, experimental acoustics, and material design. His contributions have significant implications for industrial monitoring, structural health assessment, and the development of high-precision acoustic devices. With a strong focus on innovation, interdisciplinary collaboration, and practical application, Dr. Chen exemplifies the integration of scientific research and engineering solutions, positioning him as a rising leader in the field of mechanical engineering and acoustic metamaterials.

Profile: Orcid

Featured Publications

Chen, T., Zhu, M., Li, L., Wei, H., & Xia, B. (2026). Multi-frequency weak signals enhancement detection via gradient defect phononic crystals. Measurement, 261, 119933. https://doi.org/10.1016/j.measurement.2025.119933

Chen, T., Malléjac, M., Bi, C., Xia, B., & Fleury, R. (2025). Experimental demonstration of a space-time-modulated airborne acoustic circulator. Physical Review Applied, 23, 054017. https://doi.org/10.1103/PhysRevApplied.23.054017

Chen, T., Xia, B., Yu, D., & Bi, C. (2024). Robust enhanced acoustic sensing via gradient phononic crystals. Physics Letters A, 440, 129242. https://doi.org/10.1016/j.physleta.2023.129242

Chen, T., Wang, C., & Yu, D. (2022). Pressure amplification and directional acoustic sensing based on a gradient metamaterial coupled with space-coiling structure. Mechanical Systems and Signal Processing, 181, 109499. https://doi.org/10.1016/j.ymssp.2022.109499

Chen, T., & Yu, D. (2022). A novel method for enhanced demodulation of bearing fault signals based on acoustic metamaterials. IEEE Transactions on Industrial Informatics, 18(10), 6857–6864. https://doi.org/10.1109/tii.2022.3143161

Chen, T., Jiao, J., & Yu, D. (2022). Strongly coupled phononic crystals resonator with high energy density for acoustic enhancement and directional sensing. Journal of Sound and Vibration, 529, 116911. https://doi.org/10.1016/j.jsv.2022.116911

Yalan Ye | Artificial Intelligence | Best Researcher Award

Prof Dr. Yalan Ye | Artificial Intelligence | Best Researcher Award

Professor at University of Electronic Science and Technology of China, China.

Prof. Dr. Yalan Ye is a distinguished researcher and academic with expertise in artificial intelligence, particularly in intelligent information processing and computer application technology. She serves as a Professor and Doctoral Director at the University of Electronic Science and Technology of China, where she earned her bachelor’s, master’s, and doctoral degrees. Prof. Ye’s research focuses on multimodal data fusion, cognitive state identification, and generalization of perceptual models. She has a proven track record of success, with numerous publications in prestigious journals and conferences. Prof. Ye is also actively involved in consultancy and industry projects, demonstrating her ability to bridge academic research with real-world applications.

Professional Profiles:

Education

Prof. Dr. Yalan Ye received her bachelor’s, master’s, and doctoral degrees from the University of Electronic Science and Technology of China (UESTC). During her PhD studies, she participated in a joint training program at the University of California, Irvine, USA, under the supervision of IEEE Fellow Professor Chen-Yu Phillip Sheu. This joint training was funded by the China Scholarship Council.

Professional Experience

Prof. Dr. Yalan Ye is a distinguished professor and doctoral director in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). She has been deeply involved in the research of intelligent information processing methods and their applications for many years. Her expertise spans various areas of computer science, with a significant focus on artificial intelligence, intelligent information processing, and biomedical engineering. Prof. Ye has led numerous research projects, including 4 ongoing and 11 completed projects, and has contributed extensively to consultancy and industry-sponsored projects, with 14 such engagements to her credit. Her academic contributions include the publication of 68 journals in Scopus, authorship of a book, and holding 17 published patents with 9 more under process. Prof. Ye has also taken on significant editorial roles, such as chairman of ArtInHCI 2023, local chairman of ICITES 2021, and guest editor of Electronics. She has collaborated with notable professionals in her field, including IEEE/ACM/OSA Fellow Heng Tao Shen, and is an active member of IEEE.

Research Interest

Prof. Dr. Yalan Ye’s research interests encompass a broad range of topics within the realm of artificial intelligence and its applications. Her primary focus lies in the development and advancement of intelligent information processing methods. Specifically, she is dedicated to exploring computer application technology, artificial intelligence, and machine learning, with a particular emphasis on transfer learning, domain adaptation, and zero-shot learning. Additionally, Prof. Ye’s work delves into the biomedical engineering field, where she investigates the human state intelligence perception and cognition through multimodal data fusion. She is also committed to addressing challenging issues related to cognitive state identification, the generalization of perceptual models, and the stable identification of cognitive states. Her research has resulted in a series of internationally influential outcomes, featured in top-tier journals and conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, ACM Multimedia, IJCAI, ICASSP, and EMBC. Prof. Ye’s theoretical contributions have been widely cited and positively evaluated by numerous IEEE Fellows, including Prof. Yong Lian, a member of the Canadian Academy of Engineering and former president of the IEEE Circuits and Systems Society.

Award and Honors

Prof. Dr. Yalan Ye has received numerous awards and honors recognizing her outstanding contributions to artificial intelligence and intelligent information processing. She has earned Best Paper Awards at various international conferences for her innovative research papers on intelligent information processing and machine learning. Additionally, she has been honored with the Outstanding Researcher Award by her institution, the University of Electronic Science and Technology of China, for her significant contributions to computer science and engineering. Prof. Ye is also recognized as an IEEE Senior Member, a testament to her substantial achievements and expertise in electrical and electronics engineering. Furthermore, she has been awarded the China National Science Fund for Distinguished Young Scholars for her exceptional research capabilities and potential leadership in her field. Her research papers, highly cited in prominent journals and conferences, have made a significant impact on the scientific community, earning her the title of Top Cited Author. Prof. Ye has also served as a Guest Editor for special issues of leading journals and chaired several international conferences, such as ArtInHCI 2023 and ICITES 2021. Her collaborations with renowned IEEE/ACM/OSA Fellows further cement her status as a leading researcher in her field. Prof. Dr. Yalan Ye’s contributions have advanced the understanding and application of artificial intelligence, earning her respect and recognition from the global scientific community.

Research Skills

Prof. Dr. Yalan Ye excels in a wide range of research skills, particularly in artificial intelligence and intelligent information processing. Her expertise encompasses developing and applying machine learning algorithms, including transfer learning, domain adaptation, and zero-shot learning. She is adept at multimodal data fusion, enhancing cognitive state identification and model generalization. With a strong background in biomedical engineering, Prof. Ye applies AI to solve complex health problems. Her rigorous research methodologies and innovative solutions are reflected in her numerous publications in top-tier journals and conferences. Additionally, she has extensive experience in leading and managing academic and industry-sponsored research projects, showcasing her project management and collaborative research abilities.

Publications

  1. Online multi-hypergraph fusion learning for cross-subject emotion recognition
    • Authors: Pan, T., Ye, Y., Zhang, Y., Xiao, K., Cai, H.
    • Year: 2024
    • Citations: 0
  2. Physiological Signal-Based Biometric Identification for Discovering and Identifying a New User
    • Authors: Mu, X., Jiang, H., Li, F., Xiong, G., Ye, Y.
    • Year: 2024
    • Citations: 0
  3. Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies
    • Authors: Ye, Y., Pan, T., Meng, Q., Li, J., Shen, H.T.
    • Year: 2024
    • Citations: 1
  4. Multimodal Physiological Signals Fusion for Online Emotion Recognition
    • Authors: Pan, T., Ye, Y., Cai, H., Yang, Y., Wang, G.
    • Year: 2023
    • Citations: 0
  5. Vibroarthrography-based Knee Lesions Location via Multi-Label Embedding Learning
    • Authors: Pan, T., Zhang, Y., Dong, Q., Wan, Z., Ding, T.
    • Year: 2023
    • Citations: 0
  6. Cross-subject EMG hand gesture recognition based on dynamic domain generalization
    • Authors: Ye, Y., He, Y., Pan, T., Yuan, J., Zhou, W.
    • Year: 2023
    • Citations: 0
  7. Cross-Subject Mental Fatigue Detection based on Separable Spatio-Temporal Feature Aggregation
    • Authors: Ye, Y., He, Y., Huang, W., Wang, C., Wang, G.
    • Year: 2023
    • Citations: 1
  8. Learning MLatent Representations for Generalized Zero-Shot Learning
    • Authors: Ye, Y., Pan, T., Luo, T., Li, J., Shen, H.T.
    • Year: 2023
    • Citations: 5
  9. Alleviating Style Sensitivity then Adapting: Source-free Domain Adaptation for Medical Image Segmentation
    • Authors: Ye, Y., Liu, Z., Zhang, Y., Li, J., Shen, H.
    • Year: 2022
    • Citations: 3
  10. ECG-based Cross-Subject Mental Stress Detection via Discriminative Clustering Enhanced Adversarial Domain Adaptation
    • Authors: Ye, Y., Luo, T., Huang, W., Sun, Y., Li, L.
    • Year: 2022
    • Citations: 5