师资队伍

1、主持或参与项目

[1] 51大神吃瓜在线观看官方版-51暗黑吃瓜官网2026最新版V.7.19.9.8 安卓版-糖心系列vlog网，科技产融合试点工程重大创新项目“海洋卫星遥感智能定标验证关键技术、装备研发及示范应用”课题“基于人工智能的海洋水色卫星遥感定标验证大模型研究”，2025至2027，248万，参与（技术执行负责人）

[2] 51大神吃瓜在线观看官方版-51暗黑吃瓜官网2026最新版V.7.19.9.8 安卓版-糖心系列vlog网，科技产融合试点工程重大创新项目“端边云架构的海洋智能感知与信息处理关键技术”课题“多源多模态海洋数据智能融合分析技术研究”，2023至2025，430万，负责

[3] 51大神吃瓜在线观看官方版-51暗黑吃瓜官网2026最新版V.7.19.9.8 安卓版-糖心系列vlog网，科技产融合试点工程重大创新项目“海洋生态环境监测装备关键技术突破及产品化研制”任务“高通量海洋数据智能分析技术研究”,2023至2025，200万，参与（技术执行负责人）

[4] 国家自然科学基金，区域创新发展联合基金项目“细粒度化多维异构设备计算资源共享与调度机制研究”，2025至2028，参与

[5] 山东省科技厅，山东省重点研发计划（重大科技创新工程），政务大模型关键技术及应用，2024至2026，1000万，参与

[6] 济南市科技局, 新高校20条项目, 行业大模型智能计算平台关键技术攻关与创新应用, 2024-01 至 2026-12, 90万元, 在研, 参与

[7] 科技部, 科技创新2030-“新一代人工智能（2030）”重大项目, 认知大模型关键技术研究, 2023-03 至 2024-02, 98万元, 在研, 参与

[8] 国家发改委, **重大工程（课题）, 海洋数据质量控制平台研建, 2019-01 至 2023-03, 2912万元, 结题, 参与

[9] 华为，委托开发项目，HPC集群能效分析方法研究，2022至2023，结题，参与（技术执行负责人）

[10] 中国工程院，战略研究与咨询项目，山东省海洋大数据应用服务发展战略研究，2022至2023，结题，参与

[11] 中科弘云，技术服务项目，基于人工智能的数据在线处理与分析关键技术研究，55万，结题，负责

[12] 中央军委科技委, GF科技创新特区重大专项, 云边协同大数据系统研发, 2020-12 至 2022-12, 200万元, 结题, 参与（技术执行负责人）

[13] 山东省科技厅, 山东省重点研发计划（重大科技创新工程）,大数据与人工智能研究平台构建, 2019-06 至 2021-12, 238万元, 结题, 参与

[14] 科技部，973项目，基于生物、心理多模态信息的潜在抑郁风险预警理论与生物传感关键技术研究，子课题：面向潜在抑郁风险预警的多模态特征关联性挖掘算法，2014/01-2018/12，95万元，已结题，参与（技术执行负责人）

2、奖励

[1] 海洋观监测数据质量控制与融合关键技术研究与应用示范，山东省科技进步二等奖, 山东省人民政府, 2025

3、 论文、著作

[1] Zhang, Y., Yu, Y., Wang, X., Li, X*., Liang, H., & Tiwari, P. (2026). Multi-Affection Prompt Learning for Sentiment, Emotion, and Sarcasm Joint Detection in Conversations. Tsinghua Science and Technology, 31(3), 1819-1837.[SCI-1区]

[2] Wang, H., Li, X*., Fu, X., Yang, M., Zhao, Z., Wu, X., ... & Tiwari, P. (2026). MARINE-Transformer: A General-purpose Framework for Multivariate Ocean Time Series Analysis. Neural Networks, 108706.[SCI-1区]

[3] Song, J., Huang, M., Li, X*., Zhang, Z., Wang, C., & Zhao, Z. (2025). Fusion of time-frequency features in contrastive learning for shipboard wind speed correction. Journal of Ocean University of China, 24(2), 377-386..[SCI-2区]

[4] Liu, Y., Xu, C., Liu, L., Wang, Y., Chen, F., Jia, Q., ... & Li, X. (2025, November). DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 14072-14098).[CCF-B]

[5] Li, X., Fu, X., Lin, C., Wang, X., Zhang, Y., Wang, H., ... & Wang, Y. (2025, September). A Dynamic Ensemble and Replaying Model for Online Marine Sensor Data Prediction. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 456-473). Berlin, Heidelberg: Springer Berlin Heidelberg.[CCF-B]

[6] Wang, H., Li, X*., Fu, X., Zhao, Z., Wang, C., Geng, L., & Zhang, J. (2025, October). Ocean-Llama: A Self-supervised Pre-trained Deep Learning Model for Ocean Observation Data. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 272-286). Singapore: Springer Nature Singapore.[CCF-C]

[7] Xu, Y., Li, X*., Wu, L., Wang, X., Zhao, Z., & Zhang, J. (2025, August). Seaformer: An Adaptive Forecasting Framework for Multi-source Heterogeneous Ocean Observation Data. In International Conference on Knowledge Science, Engineering and Management (pp. 431-442). Singapore: Springer Nature Singapore.[CCF-C]

[8] Chang, W., Li, X*., Chaudhary, V., Dong, H., Zhao, Z., & Nguyen, T. G. (2025). Prediction of chlorophyll‐a data based on triple‐stage attention recurrent neural network. IET Communications, 19(1), e12542.[CCF-C]

[9] Wu, S., Li, X.*, & Zhao, Z. (2024, December). Self-supervised Pretraining-Enhanced Intelligent Quality Control for Ocean Observations with Limited Historical Data. In International Conference on Neural Information Processing (pp. 92-106). Singapore: Springer Nature Singapore.[CCF-C]

[10] Wang, X., Wu, L., Wang, C., Zhao, Z., Xu, Y., Zhang, Y., & Li, X*. (2025, July). Pre-Trained Language Model for Missing Value Imputation in Ocean Buoy Data. In International Conference on Intelligent Computing (pp. 343-354). Singapore: Springer Nature Singapore.[CCF-C]

[11] Li, X., Zhao, Z., Wang C., et al. (2024). A Supervised Information Enhanced Multi-granularity Contrastive Learning Framework for EEG based Emotion Recognition. In 2024 IEEE Internationa Conference on Acoustics, Speech and Signal Processing (ICASSP). [CCF-B]

[12] Song, J., Li, X.*, Zhang Z., et al. (2024). A Predictive Framework for Shipborne Wind Speed Measurement Correction Based on Self-Supervised Contrastive Learning. In 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD). [CCF-C]

[13] Zhang, J., Song, J., Li, X.*, et al (2023). Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework. In 2023 29th IEEE International Conference on Parallel and Distributed Systems (ICPADS). [CCF-C]

[14] Song, J., Li, X.*, Jiang W., et al. (2023). EEG based Parkinson Detection through Supervised Information Enhanced Contrastive Learning. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [CCF-B]

[15] Li, X., Zhang, Y., Tiwari, P., et al. (2022). EEG based Emotion Recognition: A Tutorial and Review. ACM Computing Surveys. [ESI高被引论文，SCI-1区，IF=23.8，谷歌学术被引590次]

[16] Zhang, Y., Tiwari, P., Song, D., Mao, X., Wang, P., Li, X., & Pandey, H. M. (2021). Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis. Neural Networks, 133, 40-56.[SCI-1区，CCF-B]

[17] Li, X., Zhang, Y., Li, J. (2021a). Emotion Recognition from Multi-channel EEG Data through A Dual-pipeline Graph Attention Network. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [CCF-B]

[18] Li, X., Zhang, Y., Li, J. (2021b). Supercomputer Supported Online Deep Learning Techniques for High Throughput EEG Prediction. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [CCF-B]

[19] Li, X., Zhao, Z., Song, D., et al. (2020). Latent factor decoding of multi-channel EEG for emotion recognition through autoencoder-like neural networks. Frontiers in Neuroscience, 14, 87. [SCI-2区，IF=4.677]

[20] Zhang, Y., Song, D., Li, X., et al. (2020). A Quantum-like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis. Information Fusion, 62, 14-31. [SCI-1区]

[21] Li, X., Zhao, Z., Song, D., et al. (2019). Variational autoencoder based latent factor decoding of multichannel EEG for emotion recognition. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [CCF-B]

[22] Li, X., Song, D., Zhang, P., et al. (2018). Exploring EEG features in cross-subject emotion recognition. Frontiers in Neuroscience, 12, 162. [SCI-2区，IF=4.677，谷歌学术被引490次]

[23] Li, X., Song, D., Zhang, P., et al. (2017). Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring. International Journal of Data Mining and Bioinformatics, 18(1), 1-27. [SCI-4区]

[24] Li, X., Song, D., Zhang, P., et al. (2016). Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [CCF-B，谷歌学术被引390次]

[25] Li, X., Zhang, P., Song, D., et al. (2015a). EEG based emotion identification using unsupervised deep feature learning. ACM SIGIR Workshop on Neuro-Physiological Methods in IR Research, 2015. [CCF-A]

[26] Li, X., Zhang, P., Song, D., et al. (2015b). Recognizing emotions based on multimodal neurophysiological signals. Advances in Computational Psychophysiology, 28-30. [Science增刊]

[27] Yu, G., Li, X.*, Song, D., et al. (2016). Encoding physiological signals as images for affective state recognition using convolutional neural networks. In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 812-815. [生物医学工程领域顶会]

4、专利

[1] 基于稀疏专家混合的海洋异质时序数据自适应预测方法

[2] 基于在线增量学习的HPC作业功耗预测方法及系统

[3] 基于记忆重放变分自动编码器的IoT数据在线预测系统

[4] 一种面向海洋观测数据的基座模型构建方法及系统

[5] 预训练驱动的海洋异构时序数据多任务建模方法及系统

[6] 基于遥感条件信息扩散的海洋时空数据插补方法

[7] 一种IoT观测数据的在线学习方法及系统

[8] 一种海洋时序观测数据的异常检测方法、系统和设备

[9] 一种多通道海洋观测时序标量数据缺失值预测方法及系统

[10]  基于超算的云边协同高通量海洋数据智能处理方法及系统

5、学生培养情况

本课题组以培养具备卓越研究能力与高质量创新成果的复合型人才为核心，聚焦沟通协作、学术素养、工程实践与创新创造能力的全面养成，引导学生在研究点上深挖拓新。坚持师生平等、并肩作战，通过“亦师亦友”的深度协作共同攻克科研难题。认同“以赛促研”理念，鼓励学生参与高水平学术交流与科技竞赛，斩获近30项全国及省级科技创新奖项（含国家级一等奖2项、二等奖3项及省级一等奖7项）。已助力3名研究生荣获国家奖学金，全方位磨炼学生在复杂任务下的科研思维、工程转化、团队协作及抗压能力。