梁吉业 - 计算机与信息技术学院（大数据学院）

梁吉业，博士，教授，博士生导师，IEEE Fellow，中国计算机学会（CCF）会士，中国人工智能学会（CAAI）会士，山西大学学术委员会主任，山西大学计算智能与中文信息处理教育部重点实验室主任，曾任山西大学副校长（正校级）、太原师范学院院长。现任教育部科技委人工智能与区块链/科技伦理专门委员会委员，教育部高等学校计算机类专业教指委委员，中国计算机学会理事，中国人工智能学会常务理事，中国计算机学会人工智能与模式识别专委会主任，山西省计算机学会理事长，享受国务院政府特殊津贴专家。任国际学术期刊《Engineering Applications of Artificial Intelligence》，国内学术期刊《计算机研究与发展》、《模式识别与人工智能》与《智能系统学报》等期刊编委；是山西省高等学校优秀创新团队带头人、山西省首批科技创新重点团队带头人；入选山西省“三晋英才”支持计划高端领军人才、山西省高等学校中青年拔尖创新人才、山西省新世纪学术技术带头人333人才工程；获得山西省五一劳动奖章、山西省青年科学家奖、山西省模范教师、山西省优秀研究生导师等多项荣誉称号。

主要从事人工智能、机器学习、大数据分析挖掘等方面的教学科研工作。近年来先后主持科技部科技创新2030—“新一代人工智能”重大项目1项、国家自然科学基金/联合基金重点项目4项、国家863计划项目2项、国家自然科学基金面上项目6项等。先后在AI、JMLR、IEEE TPAMI、IEEE TKDE、ML、NeurIPS、ICML、CVPR、AAAI等国际国内重要学术期刊和会议发表论文400余篇，其中SCI收录300余篇。作为第一完成人获山西省自然科学一等奖3项、中国国际发明展览会金奖1项、山西省教学成果特等奖2项；作为第二完成人获山西省科技进步一等奖2项。2014—2023年连续入选爱思唯尔中国高被引学者榜单。指导的博士生获得全国百篇优秀博士学位论文提名奖、CCF优秀博士学位论文奖、中国人工智能学会优秀博士学位论文奖、中国中文信息学会优秀博士学位论文奖。

[1] Fuyuan Cao, Xuechun Jing*, Kui Yu, Jiye Liang. FWCEC: An Enhanced Feature Weighting Method via Causal Effect for Clustering. IEEE Transactions on Knowledge and Data Engineering, 2025, 37(2): 685-697

[2] Yuxin Fan, Junbiao Cui, Jiye Liang*. Learning textual prompts for open-world semi-supervised learning. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR‘25), 2025

[3] Jiye Liang*, Yixiao Li, Junbiao Cui. Label noise correction via fuzzy learning machine. In Proc. of the 39th AAAI Conf. on Artificial Intelligence (AAAI’25), Philadelphia, PA, USA, Feb. 25-Mar. 4, 2025

[4] Jianqing Liang, Xinkai Wei, Min Chen, Zhiqiang Wang, Jiye Liang*. GNN-Transformer cooperative architecture for trustworthy graph contrastive learning. In Proc. of the 39th AAAI Conf. on Artificial Intelligence (AAAI‘25), Philadelphia, PA, USA, Feb. 25-Mar. 4, 2025

[5] Da Wang, Lin Li, Wei Wei*, Qixian Yu, Jianye Hao, Jiye Liang. Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning[C]. In Proc. of the 39th AAAI Conf. on Artificial Intelligence (AAAI’25), Philadelphia, PA, USA, Feb. 25-Mar. 4, 2025

[6] Zhiqiang Wang, Jiayu Guo, Jianqing Liang*, Jiye Liang, Shiying Cheng, Jiarong Zhang. Graph Segmentation and Contrastive Enhanced Explainer for Graph Neural Networks[C]. In Proc. of the 39th AAAI Conf. on Artificial Intelligence (AAAI‘25), Philadelphia, PA, USA, Feb. 25-Mar. 4, 2025

[7] Zijin Du, Jianqing Liang, Jiye Liang*, Kaixuan Yao, Feilong Cao. Graph regulation network for point cloud segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 7940-7955

[8] Liangliang Wen, Jiye Liang*, Kaixuan Yao, Zhiqiang Wang. Black-box adversarial attack on graph neural networks with node voting mechanism. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(10): 5025-5038

[9] Fuyuan Cao, Qingqiang Chen*, Ying Xing, Jiye Liang. Efficient classification by removing bayesian confusing samples. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(3): 1084-1098

[10] Haijun Zhang, Xian Yang, Liang Bai*, Jiye Liang. Enhancing drug recommendations via heterogeneous graph representation learning in EHR networks. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7): 3024-3035

[11] Wei Wei, Da Wang, Lin Li, Jiye Liang*. Re-attentive experience replay in off-policy reinforcement learning. Machine Learning, 2024, 113(5): 2327-2349

[12] Jianqing Liang, Min Chen, Jiye Liang*. Graph external attention enhanced transformer. International Conference on Machine Learning (ICML2024), 2024

[13] Da Wang, Lin Li, Wei Wei*, Qixian Yu, Jianye Hao, Jiye Liang. Improving generalization in offline reinforcement learning via adversarial data splitting. International Conference on Machine Learning (ICML2024), 2024

[14] Jiye Liang*, Zijin Du, Jianqing Liang, Kaixuan Yao, Feilong Cao. Long and short-range dependency graph structure learning framework on point cloud. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(12): 14975-14989

[15] Qingqiang Chen, Fuyuan Cao*, Ying Xing, Jiye Liang*. Evaluating classification model against bayes error rate. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 9639-9653

[16] Liang Bai, Minxue Qi, Jiye Liang*. Spectral clustering with robust self-learning constraints. Artificial Intelligence, 2023, 320: 103924

[17] Wei Wei, Qin Yue, Kai Feng, Junbiao Cui, Jiye Liang*. Unsupervised dimensionality reduction based on fusing multiple clustering results. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(3): 3211-3223

[18] Yu Xie, Zhiguo Qin, Maoguo Gong, Bin Yu, Jiye Liang*. Random deep graph matching. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 10411-10422

[19] Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang*. A general representation learning framework with generalization performance guarantees. International Conference on Machine Learning (ICML2023), 2023

[20] Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang*. Set-membership belief state-based reinforcement learning for POMDPs. International Conference on Machine Learning (ICML2023), 2023

[21] Ming Li*, Sho Sonoda*, Feilong Cao, Yu Guang Wang, Jiye Liang. How powerful are shallow neural networks with bandlimited random weights?. International Conference on Machine Learning (ICML2023), 2023

[22] Liang Bai, Jiye Liang*, Yunxiao Zhao. Self-constrained spectral clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 5126-5138

[23] Xinyan Liang, Yuhua Qian*, Qian Guo, Honghong Cheng, Jiye Liang. AF: An association-based fusion method for multi-modal classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12): 9236-9254

[24] Jieting Wang, Yuhua Qian*, Feijiang Li, Jiye Liang, Qingfu Zhang. Generalization performance of pure accuracy and its application in selective ensemble learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(2): 1798-1816

[25] Kaixuan Yao, Jiye Liang*, Jianqing Liang, Ming Li, Feilong Cao. Multi-view graph convolutional networks with attention mechanism. Artificial Intelligence, 2022, 307: 103708

[26] Junbiao Cui, Jiye Liang*. Fuzzy learning machine. Advances in Neural Information Processing Systems, 2022, 35: 36693-36705

[27] Wei Wei, Yujia Zhang, Jiye Liang*, Lin Li, Yuze Li. Controlling underestimation bias in reinforcement learning via quasi-median operation. In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI’22), online, Feb. 22-Mar. 1, 2022

[28] Qingqiang Chen, Fuyuan Cao*, Ying Xing, Jiye Liang. Instance selection: A bayesian decision theory perspective. In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI‘22), online, Feb. 22-Mar. 1, 2022

[29] Yunxia Wang, Fuyuan Cao*, Kui Yu, Jiye Liang. Efficient causal structure learning from multiple interventional datasets with unknown targets. In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI’22), online, Feb. 22-Mar. 1, 2022

[30] Qin Yue, Jiye Liang*, Junbiao Cui, Liang Bai. Dual bidirectional graph convolutional networks for zero-shot node classification. KDD 2022: 2408-2417

[31] Jiye Liang*, Xiaolin Liu, Liang Bai, Fuyuan Cao, Dianhui Wang. Incomplete multi-view clustering via local and global co-regularization. SCIENCE CHINA Information Sciences, 2022, 65: 152105

[32] Liang Bai, Jiye Liang*, Fuyuan Cao. Semi-supervised clustering with constraints of different types from multiple information sources. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(9): 3247-3258

[33] Gaoxia Jiang, Wenjian Wang*, Yuhua Qian, Jiye Liang. A unified sample selection framework for output noise filtering: An error-bound perspective. Journal of Machine Learning Research, 2021, 22(18): 1-66

[34] Jiye Liang*, Junbiao Cui, Jie Wang, Wei Wei. Graph-based semi-supervised learning via improving the quality of the graph dynamically. Machine Learning, 2021, 110: 1345-1388

[35] Liang Bai*, Jiye Liang*. Sparse subspace clustering with entropy-norm. In Proc. of the 37th International Conference on Machine Learning (ICML2020), Vienna, Austria, 2020-07-12-2020-07-17

[36] Liang Bai, Jiye Liang*. A three-level optimization model for nonlinearly separable clustering, In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI‘20), New York, NY, USA, Feb. 7-Feb. 12, 2020

[37] Jing Liu, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, Jiye Liang*, A cluster-weighted kernel K-Means method for multi-view clustering. In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI’20), New York, NY, USA, Feb. 7-Feb. 12, 2020

[38] Jiye Liang*, Yunsheng Song, Deyu Li, Zhiqiang Wang, Chuangyin Dang. An accelerator for the logistic regression algorithm based on sampling on-demand. SCIENCE CHINA Information Sciences, 2020, 63(6): 169102

[39] Liang Bai, Jiye Liang*, Hangyuan Du, Yike Guo. An information-theoretical framework for cluster ensemble, IEEE Transactions on Knowledge and Data Engineering, 2019, 31(8): 1464-1477

[40] Liang Bai, Xueqi Cheng, Jiye Liang*, Huawei Shen. An optimization model for clustering categorical data streams with drifting concepts. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11): 2871-2883

[41] Zhiqiang Wang, Jiye Liang*, Ru Li, Yuhua Qian. An approach to cold-start link prediction: establishing connections between non-topological and topological information. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11): 2857-2870

[42] Yuhua Qian, Hang Xu, Jiye Liang*, Bing Liu, Jieting Wang. Fusing monotonic decision trees. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(10): 2717-2728

[43] Liang Bai, Jiye Liang*. Cluster validity functions for categorical data: A solution-space perspective. Data Mining and Knowledge Discovery, 2015, 29(6): 1560-1597

[44] Jiye Liang*, Feng Wang, Chuangyin Dang, Yuhua Qian. A group incremental approach to feature selection applying rough set technique. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 294-308

[45] Liang Bai, Jiye Liang*, Chuangyin Dang, Fuyuan Cao. The impact of cluster representatives on the convergence of the K-Modes type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1509-1522

[46] Yuhua Qian, Jiye Liang*, Witold Pedrycz, Chuangyin Dang. Positive approximation: An accelerator for attribute reduction in rough set theory. Artificial Intelligence, 2010, 174: 597-618

[47] 王锋*, 姚珍, 梁吉业. 面向动态混合数据的多粒度增量特征选择算法. 软件学报, 2025, 36(3): 1186−1201

[48] 赵兴旺, 王淑君, 刘晓琳, 梁吉业*. 基于二部图的联合谱嵌入多视图聚类算法. 软件学报, 2024, 35(9): 4408-4424

[49] 闫涛, 钱宇华*, 李飞江, 闫泓任, 王婕婷, 梁吉业, 郑珂银, 吴鹏, 陈路, 胡治国, 乔志伟, 张江峰, 翟小鹏. 三维时频变换视角的智能微观三维形貌重建方法. 中国科学: 信息科学, 2023, 53(2): 282-308

[50] 赵兴旺, 张珧溥, 梁吉业*. 基于2阶段集成的多层网络社区发现算法. 计算机研究与发展, 2023, 60(12): 2832-2843

[51] 王克琪, 钱宇华*, 梁吉业, 刘畅, 黄琴, 陈路, 贾洁茹. 局部-全局关系耦合的低照度图像增强. 中国科学: 信息科学, 2022, 52(3): 443-460

[52] 刘晓琳, 白亮, 赵兴旺, 梁吉业*. 基于多阶近邻融合的不完整多视图聚类算法. 软件学报, 2022, 33(4): 1354-1372

[53] 冯晨娇, 宋鹏, 王智强, 梁吉业*. 一种基于3因素概率图模型的长尾推荐方法. 计算机研究与发展, 2021, 58(9): 1975-1986

[54] 李飞江, 钱宇华*, 王婕婷, 梁吉业, 王文剑. 基于样本稳定性的聚类方法. 中国科学: 信息科学, 2020, 50(8): 1239-1254

[55] 成红红, 钱宇华*, 胡治国, 梁吉业. 基于邻域视角的关联关系挖掘方法. 中国科学: 信息科学, 2020, 50(6): 824-844

[56] 孟银凤, 梁吉业*. 线性正则化函数Logistic模型. 计算机研究与发展, 2020, 57(8): 1617-1626

[57] 王智强, 梁吉业*, 李茹. 基于信息融合的概率矩阵分解链路预测方法. 计算机研究与发展, 2019, 56(2): 306-318

[58] 胡清华*, 王煜, 周玉灿, 赵红, 钱宇华, 梁吉业. 大规模分类任务的分层学习方法综述. 中国科学: 信息科学, 2018, 48(5): 487-500

[59] 张凯涵, 梁吉业*, 赵兴旺, 王智强. 一种基于社区专家信息的协同过滤推荐算法. 计算机研究与发展, 2018, 55(5): 968-976

[60] 梁吉业*, 乔杰, 曹付元, 刘晓琳. 面向短文本分析的分布式表示模型. 计算机研究与发展, 2018, 55(8): 1631-1640

[61] 王智强, 李茹, 梁吉业*, 张旭华, 武娟, 苏娜. 基于汉语篇章框架语义分析的阅读理解问答研究. 计算机学报, 2016, 39(4): 795-807

[62] 梁吉业*, 冯晨娇, 宋鹏. 大数据相关分析综述. 计算机学报, 2016, 39(1): 1-18

[63] 赵兴旺, 梁吉业*. 一种基于信息熵的混合数据属性加权聚类算法. 计算机研究与发展. 2016, 53(5): 1018-1028

[64] 史倩玉, 梁吉业*, 赵兴旺. 一种不完备混合数据集成聚类算法. 计算机研究与发展, 2016, 53(9): 1979-1989

[65] 梁吉业*, 钱宇华, 李德玉, 胡清华. 大数据挖掘的粒计算理论与方法. 中国科学 (E辑: 信息科学), 2015, 45(11): 1355-1369

[66] 高小方, 梁吉业*. 基于等维度独立多流形的DC-ISOMAP算法. 计算机研究与发展, 2013, 50(8): 1690-1699

[67] 钱宇华, 梁吉业*, 王锋. 面向非完备决策表的正向近似特征选择加速算法. 计算机学报, 2011, 34(3): 435-442

[68] 梁吉业*, 白亮, 曹付元. 基于新的距离度量的K-Modes聚类算法. 计算机研究与发展, 2010, 47(10): 1749-1755

[69] 梁吉业*, 钱宇华. 信息系统中的信息粒与熵理论. 中国科学 (E辑: 信息科学), 2008, 38(12): 2048-2065

[70] 曲开社, 翟岩慧, 梁吉业*, 李德玉. 形式概念分析对粗糙集理论的表示及扩展. 软件学报, 2007, 18(9): 2174-2182

[71] 曹飞龙, 徐宗本, 梁吉业*. 多项式函数的神经网络逼近: 网络的构造与逼近算法. 计算机学报, 2003, 26(8): 906-912

[72] 梁吉业*, 徐宗本, 李月香. 包含度与粗糙集数据分析中的度量. 计算机学报, 2001, 24(5): 544-547

1. 国家自然科学基金委员会，联合基金重点项目，U21A20473，网络大数据分析挖掘的理论与方法，2022-01至2025-12，主持

2. 国家自然科学基金委员会，面上项目，62376141，知识引导的开放集学习方法研究，2024.01至2027.12，主持

3. 国家科技部，科技创新2030—“新一代人工智能”重大项目，2020AAA0106100，认知计算基础理论与方法研究，2020-11至2024-10，主持

4. 国家自然科学基金委员会，面上项目，61876103，基于多粒度的半监督学习方法，2019-01至2022-12，主持

5. 国家自然科学基金委员会，重点项目/总装联合基金项目，61432011/U1435212，面向大数据的粒计算理论与方法，2015-01至2019-12，主持

6. 国家自然科学基金委员会，重点项目，71031006，高维复杂数据分析理论及其在投资决策中的应用，2011-01至2014-12，主持

7. 国家科技部，973计划前期研究专项，2011CB11805，基于认知机理的高维复杂数据建模理论与方法，2011-01至2012-12，主持

8. 国家自然科学基金委员会，面上项目，70971080，面向复杂数据的粗糙集多属性/多准则决策分析研究，2010-01至2012-12，主持

9. 国家自然科学基金委员会，面上项目，60773133，复杂信息系统的粒度结构与知识获取研究，2008-01至2010-12，28万元，已结题，主持

10. 国家科技部，863计划项目，2007AA01Z165，面向高维复杂数据的粒度计算理论与算法研究，2007-10至2009-12，主持

11. 国家自然科学基金委员会，面上项目，70471003，基于软计算技术的不确定性决策方法研究，2005-01至2007-12，主持

12. 国家科技部，863计划项目，2004AA115460，专家系统及计算机软硬件系统评价技术研究，2004-10至2005-12，主持

13. 国家自然科学基金委员会，面上项目，60275019，粗糙集理论中的不确定性、模糊性与知识获取，2003-01至2005-12，主持