导师简介：谢小峰

谢小峰：1988年生，海南临高人，副教授，博士生导师，海南大学本科生院创新创业院副院长，中国人工智能学会青年工作委员会委员、中国自动化学会青年工作委员委员、海南省人工智能学会理事，曾获海南省科技进步二等奖、海南省教学成果奖二等奖、海南大学“十佳好老师”、海南大学优秀学业导师等荣誉。

主要研究领域为模式识别与脑机接口，涉及应用领域包括大数据处理、生物医学工程以及智能检测等。目前以作第一者身份在Pattern Recognition、 IEEE Iransaction NSRE 等人杂志上发表论文30余篇，并在相关国际学术会议上发表30余篇会议论文，申请/授权20项国家发明专利，主持国家自然科学基金、国家重点研发项目子课题、海南省重点研发项目、海南省科协青年科技英才创新计划等项目。

本人长期围绕人工智能、医学影像分析、脑机接口与智能机器人等方向开展研究，具有扎实的算法研究基础和丰富的工程实践经验，并与多家企业及事业单位保持长期深入合作，能够为学生提供真实科研课题、工程实践平台和产学研协同培养机会。注重学生科研创新能力、算法建模能力与工程落地能力的综合培养，已指导学生在“创新大赛”“挑战杯”、研究生数学建模、人工智能与机器人竞赛等国家级、省部级赛事中获得30余项奖励。

长期招募硕士研究生、博士研究生、国际硕博学生、博士后等。欢迎具有计算机、人工智能、电子信息、自动化、生物医学工等相关学科背景的海内外优秀学生加入团队，共同围绕人工智能交叉应用方向开展科研探索与创新实践。

邮箱:xfxie@hainanu.edu.cn

教育经历:

1.2011/09-2018/09，华南理工大学，模式识别与智能系统，硕博连读

2.2007/09-2011/06，华南理工大学，自动化专业

主持科研项目:

1.国家自然科学基金青年基金，基于黎曼图神经网络的轻度认知功能障碍诊断方法及编解码机制研究，2023-2025，结题，主持

2.国家重点研发项目，老年心肺功能减退及相关疾病多维预警、综合诊疗与干预策略研究，2023-2025，结题，主持（子课题负责人）

3.国家自然科学基金-重点项目，基于低剂量胸部CT多任务生成式人工智能预警慢性阻塞性肺疾病的技术研究，2025-2029，在研，主持（子课题负责人）

4.海南省重点研发计划，基于经颅磁刺激同步脑电技术的阿尔茨海默病早期诊断技术及系统研究，2022-2024年，结题，主持

5.海南省软科学项目，自贸港封关运作后的科技伦理治理风险挑战及应对措施研究，2024-2025年，结题，主持

6.海南省科协青年科技英才创新计划，面向阿尔茨海默症早期诊断的脑机接口技术，2020-2024年，结题，主持

代表性成果：

Xie X., Xue P, Guo Y, et al. Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer’s Disease Diagnosis and Multi-Modal Biomarker Identification[J]. Interdisciplinary Sciences: Computational Life Sciences, 2026: 1-16.

Wang X, Guo Y, Chen F., Xie, X*. RRAECL: A Riemannian manifold graph representation and enhanced contrastive learning framework for label-efficient Alzheimer’s disease diagnosis[J]. Neurocomputing, 2025, 640: 130409.

Xie, X., Yu, Z. L.*, Gu, Z., & Li, Y. Classification of symmetric positive definite matrices based on bilinear isometric Riemannian embedding[J]. Pattern Recognition, 2019, 87: 94-105.

Xie X., Zou X, Yu T, et al. Multiple graph fusion based on Riemannian geometry for motor imagery classification[J]. Applied Intelligence, 2022, 52(8): 9067-9079.

Xie, X., Yu, Z. L., Gu, Z., Zhang, J, Cen, L., & Li, Y. Bilinear regularized Iocality preserving learning on Riemannian graph formotor imagery BCI[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(3): 698-708.

Tang, R., Li, Z., Xie, X*. Motor Imagery EEG Signal Classification using Upper Triangle Filter Bank Auto-Encode Method[J] Biomedical Signal Processing and Control, 2021,68(102608)

Huang Y, Yu Z, Gu Z, Xie X*, Tang R, Li C. Optimized Motor Imagery Paradigm via Multimodal Stimulation and Explainable LSTM Model in fNIRS-based BCI[J]. IFAC-PapersOnLine, 2023, 56(2): 6496–6503.

Biography

Xiaofeng Xie, born in 1988 in Lingao, Hainan Province, is a Ph.D., Associate Professor, and doctoral supervisor. He currently serves as Vice Dean of the Innovation and Entrepreneurship Institute of Hainan University. He is also a member of the Third Youth Working Committee of the Chinese Association for Artificial Intelligence, a council member of the Hainan Artificial Intelligence Society. He has received several honors and awards, including the Second Prize of the Hainan Provincial Science and Technology Progress Award, the Second Prize of the Hainan Provincial Teaching Achievement Award, the “Top Ten Outstanding Teachers” Award of Hainan University, the Second and Third Prizes in the Hainan University Teaching Innovation Competition, the Outstanding Academic Advisor Award of Hainan University.

His main research interests include pattern recognition, machine learning theory and engineering applications, with application areas covering big data processing, biomedical engineering, intelligent detection, and related fields. As first author, he has published more than 30 papers in internationally recognized journals in the field of artificial intelligence, including Pattern Recognition, IEEE Transactions on Neural Systems and Rehabilitation Engineering, and Applied Intelligence. He has also published over 30 conference papers at international academic conferences and has applied for or been granted 20 national invention patents. He has led 11 research projects, including projects funded by the National Natural Science Foundation of China, the National Key Research and Development Program of China, key R&D programs of Hainan Province, the Young Scientific and Technological Talents Innovation Program of the Hainan Association for Science and Technology, the Natural Science Foundation of Hainan Province, and industry-sponsored projects.

Dr. Xie has long been engaged in research on artificial intelligence, medical image analysis, meteorological data modeling, brain-computer interfaces, intelligent robotics, and related interdisciplinary fields. He has a solid foundation in algorithmic research and extensive experience in engineering practice. He also maintains long-term and in-depth collaborations with various enterprises and public institutions, providing students with opportunities to participate in real-world research projects, engineering practice platforms, and industry–university–research collaborative training. He places strong emphasis on cultivating students’ research innovation ability, algorithmic modeling ability, and engineering implementation ability. Under his supervision, students have won more than 30 awards in national, provincial, and ministerial-level competitions.

Outstanding students from China and abroad with backgrounds in computer science, artificial intelligence, electronic information, automation, biomedical engineering, mathematics, meteorology, medical imaging, and related disciplines are warmly welcomed to join the team as master’s students, doctoral students, or joint-training students. Together, they will carry out scientific research and innovative practice in interdisciplinary applications of artificial intelligence.

Contact Information

Email: xfxie@hainanu.edu.cn

Educational Background

September 2011 – September 2018

South China University of Technology

Pattern Recognition and Intelligent Systems

Combined Master’s and Doctoral Program

September 2007 – June 2011

South China University of Technology

Automation

Research Projects

1.Young Scientists Fund of the National Natural Science Foundation of China

Diagnosis Method and Encoding–Decoding Mechanism for Mild Cognitive Impairment Based on Riemannian Graph Neural Networks

2023–2025, completed, Principal Investigator

2.National Key Research and Development Program of China

Research on Multidimensional Early Warning, Comprehensive Diagnosis and Treatment, and Intervention Strategies for Age-Related Cardiopulmonary Functional Decline and Related Diseases

2023–2025, completed, Principal Investigator of Sub-project

3.Key Program of the National Natural Science Foundation of China

Research on Multi-task Generative Artificial Intelligence-Based Early Warning Technology for Chronic Obstructive Pulmonary Disease Using Low-Dose Chest CT

2025–2029, ongoing, Principal Investigator of Sub-project

4.Key Research and Development Program of Hainan Province

Research on Early Diagnosis Technology and System for Alzheimer’s Disease Based on Transcranial Magnetic Stimulation Synchronized with Electroencephalography

2022–2024, completed, Principal Investigator

5.Soft Science Research Project of Hainan Province

Research on Risk Challenges and Countermeasures for Science and Technology Ethics Governance after the Independent Customs Operation of the Hainan Free Trade Port

2024–2025, completed, Principal Investigator

6.Young Scientific and Technological Talents Innovation Program of the Hainan Association for Science and Technology

Brain-Computer Interface Technology for Early Diagnosis of Alzheimer’s Disease

2020–2024, completed, Principal Investigator

Publication：

Xie X., Xue P, Guo Y, et al. Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer’s Disease Diagnosis and Multi-Modal Biomarker Identification[J]. Interdisciplinary Sciences: Computational Life Sciences, 2026: 1-16.

Wang X, Guo Y, Chen F., Xie, X*. RRAECL: A Riemannian manifold graph representation and enhanced contrastive learning framework for label-efficient Alzheimer’s disease diagnosis[J]. Neurocomputing, 2025, 640: 130409.

Xie, X., Yu, Z. L.*, Gu, Z., & Li, Y. Classification of symmetric positive definite matrices based on bilinear isometric Riemannian embedding[J]. Pattern Recognition, 2019, 87: 94-105.

Xie X., Zou X, Yu T, et al. Multiple graph fusion based on Riemannian geometry for motor imagery classification[J]. Applied Intelligence, 2022, 52(8): 9067-9079.

Xie, X., Yu, Z. L., Gu, Z., Zhang, J, Cen, L., & Li, Y. Bilinear regularized Iocality preserving learning on Riemannian graph formotor imagery BCI[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(3): 698-708.

Xie, X., Zou, X, Yu, T., Tang, R., Hou, Y., Li, Y., Qi, F. * Multiple Graph Fusion based on Riemannian Geometry for Motor ImageryClassification[J]. Applied Intelligence, 2021, doi:10.1007/s10489-021-02 975-2

Tang, R., Li, Z., Xie, X*. Motor Imagery EEG Signal Classification using Upper Triangle Filter Bank Auto-Encode Method[J] Biomedical Signal Processing and Control, 2021,68(102608)

Huang Y, Yu Z, Gu Z, Xie X*, Tang R, Li C. Optimized Motor Imagery Paradigm via Multimodal Stimulation and Explainable LSTM Model in fNIRS-based BCI[J]. IFAC-PapersOnLine, 2023, 56(2): 6496–6503.