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
- 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
- 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
- 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
- Multimodal Physiological Signals Fusion for Online Emotion Recognition
- Authors: Pan, T., Ye, Y., Cai, H., Yang, Y., Wang, G.
- Year: 2023
- Citations: 0
- 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
- 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
- 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
- Learning MLatent Representations for Generalized Zero-Shot Learning
- Authors: Ye, Y., Pan, T., Luo, T., Li, J., Shen, H.T.
- Year: 2023
- Citations: 5
- 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
- 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