本科、硕士毕业于南京大学计算机科学与技术专业，博士毕业于东南大学计算机科学与工程学院计算机应用技术专业，曾先后在香港大学电子商务研究所、美国佐治亚州立大学访问进修、工作。现任东南大学计算机科学与工程学院教授，中国计算机学会大数据专家委员会委员，江苏省大数据专家委员会副主任，江苏省生物信息学会委员。主要研究兴趣包括数据密集型计算、数据挖掘和机器学习、生物信息学等。本人长期从事数据分析与挖掘工作，近年来在面向大数据的网络表示学习与挖掘，大数据下的深度学习及推荐，大数据下的数据仓库建模及复杂查询分析等方面进行了较深入的研究，积累了大量经验和成果，并发表相关多篇论文。
大数据技术、数据挖掘和机器学习、生物信息学等
l 中药分子标识大数据智能挖掘研究及其在中药示范中的应用，国家重点研发计划课题
l 立法公众意见综合分析及法律条文智能审查技术研究，国家重点开发计划项目
l 面向立法的高质量智能辅助体系构建，国家重点研发计划课题
l 面向复杂生物网络的多特征大模体识别方法研究，江苏省自然科学基金面上项目
l 基于移动数据库系统的企业移动管理技术研究与开发，国家863
l 面向蛋白质结构预测的智能可理解性技术的研究，江苏省自然科学基金面上项目
l 基于语义网格的信息集成平台技术的研究，江苏省“十五”高科技项目
l [1]Jiacong Mi, Yi Zu, Zhuoyuan Wang, Jieyue He，ACDNet: Attention-guided Collaborative Decision Network for effective medication recommendation，Journal of Biomedical Informatics，Volume 149, January 2024.
l [2]Hua Pu, Jiacong Mi, Shan Lu, Jieyue He,RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine,BIBM 2023,12.
l [3]Yi Zu, Jiacong Mi, Lingning Song, Shan Lu and Jieyue He,Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction,IEEE BigData,2023,12.
l [4] Zhang, N., Wang, J. & He, J. HARPA: hierarchical attention with relation paths for knowledge graph embedding adversarial learning. Data Min Knowl Disc 37, 521–551 (2023).
l [5]Yu Wu, Xin Xi, Jieyue He, AFGSL: Automatic Feature Generation based on Graph Structure Learning, Knowledge-Based Systems, Volume 238, 2022, 107835, ISSN 0950-7051.
l [6]Jieyue He, Wang J. & Yu Z. Attention based adversarially regularized learning for network embedding, Data Min Knowledge, 2021.10.
l [7]S. Li, W. Wang and J. He, KGAPG: Knowledge-Aware Neural Group Representation Learning for Attentive Prescription Generation of Traditional Chinese Medicine, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA.
l [8]Jieyue He; Xinxing Yang; Zhuo Gong; Ibrahim Zamit. Hybrid Attentional Memory Network for Computational drug repositioning. BMC Bioinformatics 21, 566 (2020)
l [9]Xinxing Yang, lbrahim Zamit, Yu Liu, Jieyue He： Additional Neural Matrix Factorization model for computational drug repositioning, BMC Bioinformatics,20 (1), 2019.
l [10]W. Wu, Z. Yu and J. He, A semi-supervised deep network embedding approach based on the neighborhood structure, Big Data Mining and Analytics, vol. 2, no. 3, pp. 205-216, September 2019.
l [11]Y. Liu, S. Wang,MS Khan,J. He: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining & Analytics, 2018 , 1 (3) :211-221.
l [12]Bin Shen, Muwei Zhao, Wei Zhong, and Jieyue He: An Improved Method for Completely Uncertain Biological Network Alignment, BioMed Research International, vol. 2015, Article ID 253854, 11 pages, 2015.
l [13]Jieyue He, Chunyan Wang, Kunpu Qiu1 and Wei Zhong: An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks. BMC Systems Biology 2014.
l [14]Muwei Zhao，Wei Zhong，Jieyue He* :PBNA: An Improved Probabilistic Biological Network Alignment Method. Tsinghua Science and Technology, 6,2014.
l [15]Jieyue He,Chaojun Li, Baoliu Ye, Wei Zhong:Efficient and accurate greedy search methods for mining functional modules in protein interaction networks. BMC Bioinformatics 2012, 13(Suppl 10):S19.
