个人简介

博士，教授，博士生导师(学博+专博)

教育简历

1991年在北方工业大学获得自动化仪表专业本科学位,1996年、2004年在北京工业大学分别获得智能控制、计算机应用技术硕士、博士学位，2005年和2010年分别在挪威科技大学、美国纽约州立大学布法罗分校做高级访问学者。

工作履历

1996年4月起在北京工业大学任教至今。2003年被评聘为副研究员，2004年被评聘为硕士研究生导师，2008年被评聘为教授，2011年被评聘为博士研究生导师, 2024年被评聘为专业博士研究生导师。

学术兼职

担任中国人工智能学会理事，智慧医疗专委会常务委员，不确定性人工智能专委会委员，中国计算机学会人工智能与模式识别专委会委员、北京人工智能学会理事、AD协会人工智能分会常务委员、国际SCI期刊KSII Transactions on Internet and Information Systems和国内核心期刊北京工业大学学报编委。

课程教学

本科生教学：人工智能导论

研究生教学：人工智能原理、数据挖掘与知识发现、机器学习、机器学习理论与应用（博士生）

科研项目

1. 融合多源信息的半监督深度哈希学习脑网络分类方法，国家自然科学基金面上项目（62276010），2023年—2026年；

2. 基于群智能算法的脑效应连接网络学习方法研究，国家自然科学基金面上项目（61672065），2017年—2020年；

3. 面向大规模蛋白质网络功能模块检测的群智能算法研究，国家自然科学基金面上项目（61375059），2014年—2017年；

4. 抑郁风险评估相关的生物学、心理学理论依据及验证标准，国家重点基础研究发展计划（973课题第1课题组2014CB744601），2014年—2015年；

5. 基于蚁群聚类的PPI网络功能模块检测方法研究，高等学校博士学科点专项科研基金-博导类基金项目（20121103110031），2013年—2015年；

6. 面向脑网络分类的深度森林强化机制和方法研究，2022市教委科研计划重点项目即北京市基金重点B类项目（KZ202210005009），2022年—2024年；

7. 基于群智能的大规模蛋白质网络功能模块检测方法研究，2014市教委科研计划重点项目即北京市基金重点B类项目（KZ201410005004），2014年—2016年；

8. 基于蚁群算法和贝叶斯网的智能导航技术研究，北京市自然科学基金面上项目（4102010），2010年-2012年；

9. 蚁群算法及其在智能交通中的应用研究，北京市自然科学基金预探索项目（4083034），2008年-2009年；

10. 面向网络事件的跨平台异质媒体语义协同与挖掘，国家自然科学基金重点项目（61332016），2014年—2018年；（联合申请）

11. 非规范知识表示的逻辑理论，国家自然科学基金重大项目（60496322），2004年—2008年；（参与）

12. 约束/归纳逻辑程序设计中的软件计算研究，国家自然科学基金面上项目（60173014），2002年—2004年；（参与）

13. 可操作的电子邮件的理论及其智能应用研究，国家自然科学基金面上项目（60673015），2007年—2009年；（参与）

14. 古代建筑虚拟修复及WEB表现技术研究－基于语义理解的古代建筑动画辅助生成系统，国家科技支撑计划（攻关计划），2007年-2008年；（参与）

15. 知识发现中的软计算研究，北京市自然科学基金面上项目（4022003），2002年-2004年。（参与）

荣誉和获奖

2006年入选北京市优秀人才计划；

2010年入选北京市青年骨干教师计划；

2013年被评为北京工业大学第7届 “我爱我师” 我心目中最喜爱的十佳老师；

2018年11月, 获得"北京工业大学研究生教育突出贡献奖"；

2019年9月，获得"2018年北京工业大学优秀教师标兵"称号；

2021年被授予北京工业大学高层次创新人才培养优秀指导教师；

2021年获得中国人工智能学会优秀工作者；

2022年4月，获得北京图像图形学会优秀导师提名奖。

2023年9月，获得"2022年北京工业大学优秀教师标兵"称号；

代表性研究成果

作为项目负责人和项目骨干完成了国家自然科学基金重大、重点、面上、973专项子课题、国家科技支撑项目、高等学校博士学科点专向科研基金（博导类）、北京市自然科学基金重点、面上、预探索、国际、国内合作科研项目数十项。已在TKDE、TIP、TNNLS、TMI、TITS、TIM、AAAI、IJCAI、EMNLP、自动化学报、科学通报等国际、国内权威期刊或会议上发表论文200多篇,授权发明专利十余项。

主要论文论著

[1] Ji J, Zhang Z, Han L, et al. MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation[J]. Computers in Biology and Medicine, 2024, 170: 107940.

[2] Shi Y, Ji J, Zhang X, et al. Prior tissue knowledge-driven contrastive learning for brain CT report generation[J]. Multimedia Systems, 2024, 30(2): 98.

[3] Ji J, Wu T, Yang C. Multimodal Multiobjective Differential Evolutionary Optimization With Species Conservation[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54（2）: 1299-1311.

[4] Zhang X, Dou S, Ji J, et al. Co-Occurrence Relationship Driven Hierarchical Attention Network for Brain CT Report Generation[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024,8(5): 3643-3653.

[5] Ji J, Ye C, Yang C. Convolutional bidirectional GRU for dynamic functional connectivity classification in brain diseases diagnosis[J]. Knowledge-Based Systems, 2024, 287: 111450.

[6] Ji J, Wang T, Liu J, et al. River runoff causal discovery with deep reinforcement learning[J]. Applied Intelligence, 2024, 54(4): 3547-3565.

[7] Ji J, He J, Lei M, et al. Spatio-Temporal Transformer Network for Weather Forecasting[J]. IEEE Transactions on Big Data, 2024.

[8] Liu J, Han L, Ji J. MCAN: multimodal causal adversarial networks for dynamic effective connectivity learning from fMRI and EEG data[J]. IEEE Transactions on Medical Imaging, 2024. 43(8):2913-2923

[9] Liu, J., Wang, F., & Ji, J. Concept-Level Causal Explanation Method for Brain Function Network Classification. the 33rd International Joint Conference on Artificial Intelligence (IJCAI )，2024,3087-3096

[10] Ji J, Wang F, Han L, et al. Causal Learning and Knowledge Fusion Mechanism for Brain Functional Network Classification[J]. IEEE Transactions on Signal and Information Processing over Networks, 2024, 10, 650-664.

[11] Ji J, Han L, Wang F, et al. Dynamic Effective Connectivity Learning based on non-Parametric State Estimation and GAN[J]. IEEE Transactions on Instrumentation and Measurement, 2024,73, 1-12.

[12] Zou A, Ji J, Lei M, et al. Exploring brain effective connectivity networks through spatiotemporal graph convolutional models[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024,35(6): 7871-7883.

[13] Yang C, Sui G, Ji J, et al. DpEA: A dual-population evolutionary algorithm for dynamic constrained multiobjective optimization[J]. Expert Systems with Applications, 2024: 124441.

[14] Ji J, Wu T, Yang C. Neural population dynamics optimization algorithm: A novel brain-inspired meta-heuristic method[J]. Knowledge-Based Systems, 2024, 300: 112194.

[15] He J, Ji J, Lei M. Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting[C]//Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024: 819-828.

[16] Zhang Z, Ji J, Liu J. MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(9): 10261-10269.

[17] Zhao R, Ji J, Lei M. Decomposed Latent Diffusion Model for 3D Point Cloud Generation[C]//Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Singapore: Springer Nature Singapore, 2024: 431-445.

[18] Ji J, Zhao R, Lei M. Latent diffusion transformer for point cloud generation[J]. The Visual Computer, 2024: 1-15.

[19] Zheng C, Ji J, Shi Y, et al. See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

[20] Liu J, Zhai J, Ji J. Inferring causal protein signalling networks from single cell data based on parallel discrete artificial bee colony algorithm[J]. CAAI Transactions on Intelligence Technology, 2024, 1-18

[21] Ji J, Han L, Wang F, et al. Dynamic Effective Connectivity Learning based on non-Parametric State Estimation and GAN[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73, 1-12.

[22] Ji J, Yu F, Lei M. Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 1580-1593.

[23] Yang C, Wang P, Ji J. A dual decomposition strategy for large-scale multiobjective evolutionary optimization[J]. Neural Computing and Applications, 2023, 35(5): 3767-3788.

[24] Ji J, Zou A, Liu J, et al. A survey on brain effective connectivity network learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023，34(4): 1879-1899.

[25] Ji J, Jia H, Ren Y, et al. Supervised contrastive learning with structure inference for graph classification[J]. IEEE Transactions on Network Science and Engineering, 2023, 10(3): 1684-1695.

[26] Yang C, Wu T, Ji J. Two-stage species conservation for multimodal multi-objective optimization with local Pareto sets[J]. Information Sciences, 2023, 639: 118990.

[27] Ji J, Liu Y, Yang C. Dual ant colony optimization for electric vehicle charging infrastructure planning[J]. Applied Intelligence, 2023, 53(22): 26690-26707.

[28] Ji J, Zhang Y. Deep Hashing Mutual Learning for Brain Network Classification[J]. IEEE Journal of Biomedical and Health Informatics, 2023, 27(9): 4489-4499.

[29] Ji J, Wang X. Fast progressive differentiable architecture search based on adaptive task granularity reorganization[J]. Information Sciences, 2023, 645: 119326.

[30] Ji J, Li J. Tri-objective optimization-based cascade ensemble pruning for deep forest[J]. Pattern Recognition, 2023, 143: 109744.

[31] Ji J, Wang X. Convolutional architecture search based on particle swarm algorithm for functional brain network classification[J]. Applied Soft Computing, 2023, 149: 111049.

[32] Wang X, Ji J. Adaptive particle swarm architecture search based on multi-level convolutions for functional brain network classification[C]//2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2023: 654-659.

[33] Shi Y, Ji J, Zhang X, et al. Granularity matters: pathological graph-driven cross-modal alignment for brain ct report generation[C]//Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023: 6617-6630.

[34] Zhang X, Yang S, Shi Y, et al. Weakly guided attention model with hierarchical interaction for brain CT report generation[J]. Computers in Biology and Medicine, 2023, 167: 107650.

[35] 冀俊忠, 龙腾, 杨翠翠  基于邻域决策粗糙集的脑功能连接生物标记物识别方法, 控制与决策, 2023,38(4): 1092-1100.

[36] 梁玉泽, 冀俊忠, 基于原型学习与深度特征融合的脑功能连接分类方法研究, 自动化学报, 2022, 48(2): 504-514.

[37] Ji J, Wang Z, Zhang X, et al. Sparse data augmentation based on encoderforest for brain network classification[J]. Applied Intelligence, 2022,52(4): 4317-4329.

[38] Ji J, Yao Y. A novel CNN framework to extract multi-level modular features for the classification of brain networks[J]. Applied Intelligence, 2022. 52(6): 6835-6852.

[39] Ji J, Chen Z, Yang C. Convolutional neural network with sparse strategies to classify dynamic functional connectivity[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 26(3): 1219-1228.

[40] Ji J, Zhang Y. Functional brain network classification based on deep graph hashing learning[J]. IEEE Transactions on Medical Imaging, 2022, 41(10): 2891-2902.

[41] Liu J, Ji J, Xun G, et al. Inferring effective connectivity networks from fMRI time series with a temporal entropy-score[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(10): 5993-6006.

[42] Ji J, Weng Y, Yang C. Multiobjective bacterial foraging optimization with an adaptive dual grid strategy[J]. Swarm and Evolutionary Computation，2022,72: 101098.

[43] Yang C, Weng Y, Ji J, et al. A knowledge guided bacterial foraging optimization algorithm for many-objective optimization problems[J]. Neural Computing and Applications, 2022,34(23): 21275-21299.

[44] Ji J, Wang M, Zhang X, et al. Relation constraint self-attention for image captioning[J]. Neurocomputing, 2022, 501: 778-789.

[45] Ji J, Ren Y, Lei M. FC–HAT: Hypergraph attention network for functional brain network classification[J]. Information Sciences, 2022, 608: 1301-1316.

[46] Ji J, Li J. Deep Forest With Multi-Channel Message Passing and Neighborhood Aggregation Mechanisms for Brain Network Classification[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(11): 5608-5618.

[47] Zou A, Ji J, Lei M, et al. Exploring brain effective connectivity networks through spatiotemporal graph convolutional models[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022.

[48] Ji J, Jia H, Ren Y, et al. Supervised Contrastive Learning with Structure Inference for Graph Classification[J]. IEEE Transactions on Network Science and Engineering, 2023.

[49] Song X, Zhang X, Ji J, et al. Cross-modal Contrastive Attention Model for Medical Report Generation[C]//Proceedings of the 29th International Conference on Computational Linguistics. 2022: 2388-2397.

[50] Lu Y, Liu J, Ji J, et al. Brain Effective Connectivity Learning with Deep Reinforcement Learning[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 1664-1667.

[51] 冀俊忠, 邹爱笑, 刘金铎. 基于功能磁共振成像的人脑效应连接网络识别方法综述[J]. 自动化学报, 2021, 47(2): 278-296.

[52] 冀俊忠, 刘金铎, 邹爱笑, 等. 一种融合多源信息的脑效应连接网络蚁群学习算法[J]. 自动化学报, 2021, 47(4): 864-881.

[53] Ji J, Xing X, Yao Y, et al. Convolutional kernels with anelement-wise weighting mechanism for identifying abnormal brain connectivity patterns[J]. Pattern Recognition, 2021, 109: 107570.

[54] Ji J, Xiao H, Yang C. HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networks[J]. Applied Intelligence, 2021, 51: 1118-1132.

[55] Ji J, Liu J, Han L, et al. Estimating effective connectivity by recurrent generative adversarial networks[J]. IEEE Transactions on Medical Imaging, 2021, 40(12): 3326-3336.

[56] Ji J, Du Z, Zhang X. Divergent-convergent attention for image captioning[J]. Pattern Recognition, 2021, 115: 107928.

[57] Ji J, Liang Y, Lei M. Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation[J]. Neural Networks, 2021, 142: 522-533.

[58] Ji J, Yao Y. Convolutional neural network with graphical LASSO to extract sparse topological features for brain disease classification[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2020, 18(6): 2327-2338.

[59] Ji J, Yao Y. A novel CNN framework to extract multi-level modular features for the classification of brain networks[J]. Applied Intelligence, 2022: 1-18.

[60] Zou A, Ji J. Learning brain effective connectivity networks via controllable variational autoencoder[C]//2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021: 284-287.

[61] Yang S, Ji J, Zhang X, et al. Weakly guided hierarchical encoder-decoder network for brain ct report generation[C]//2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021: 568-573.

[62] Liu J, Ji J, Xun G, et al. EC-GAN: inferring brain effective connectivity via generative adversarial networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(04): 4852-4859.

[63] Liu J, Ji J, Jia X, et al. Learning brain effective connectivity network structure using ant colony optimization combining with voxel activation information[J]. IEEE journal of biomedical and health informatics, 2019, 24(7): 2028-2040.

[64] Ji J, Xu C, Zhang X, et al. Spatio-temporal memory attention for image captioning[J]. IEEE Transactions on Image Processing, 2020, 29: 7615-7628.

[65] Yang C, Ji J, Li S. Stability analysis of chemotaxis dynamics in bacterial foraging optimization over multi-dimensional objective functions[J]. Soft Computing, 2020, 24: 3711-3725.

[66] 姚垚, 冀俊忠. 基于栈式循环神经网络的血液动力学状态估计方法[J]. 自动化学报, 2020, 46(5): 991-1003.

[67] 邢新颖, 冀俊忠, 姚垚. 基于自适应多任务卷积神经网络的脑网络分类方法[J]. 计算机研究与发展, 2020, 57(7): 1449-1459.

[68] Ji J, Liu J, Zou A, et al. ACOEC-FD: Ant colony optimization for learning brain effective connectivity networks from functional MRI and diffusion tensor imaging[J]. Frontiers in Neuroscience, 2019, 13: 1290.

[69] Zhao X, Ji J, Wang X. Dynamic brain functional parcellation via sliding window and artificial bee colony algorithm[J]. Applied Intelligence, 2019, 49: 1748-1770.

[70] Zhao X, Ji J, Zhang A. Artificial bee colony clustering with self-adaptive crossover and stepwise search for brain functional parcellation in fMRI data[J]. Soft Computing, 2019, 23: 8689-8709.

[71] Liu J, Ji J, Yao L, et al. Estimating brain effective connectivity in fMRI data by non-stationary dynamic Bayesian networks[C]//2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019: 834-839.

[72] Li J, Ji J, Liang Y, et al. Deep forest with cross-shaped window scanning mechanism to extract topological features[C]//2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019: 688-691.

[73] Chen Z, Ji J, Liang Y. Convolutional neural network with an element-wise filter to classify dynamic functional connectivity[C]//2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019: 643-646.

[74] Yang C, Ji J, Zhang A. BFO-FMD: bacterial foraging optimization for functional module detection in protein–protein interaction networks[J]. Soft Computing, 2018, 22: 3395-3416.

[75] Xing X, Ji J, Yao Y. Convolutional neural network with element-wise filters to extract hierarchical topological features for brain networks[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018: 780-783.

[76] Ji J, Yang C, Liu J, et al. A comparative study on swarm intelligence for structure learning of Bayesian networks[J]. Soft Computing, 2017, 21: 6713-6738.

[77] Yang C, Ji J, Liu J, et al. Structural learning of Bayesian networks by bacterial foraging optimization[J]. International Journal of Approximate Reasoning, 2016, 69: 147-167.

[78] Yang C, Ji J, Liu J, et al. Bacterial foraging optimization using novel chemotaxis and conjugation strategies[J]. Information Sciences, 2016, 363: 72-95.

[79] Ji J, Lv J, Yang C, et al. Detecting functional modules based on a multiple-grain model in large-scale protein-protein interaction networks[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2015, 13(4): 610-622.

[80] Ji J, Liu J, Liang P, et al. Learning effective connectivity network structure from fMRI data based on artificial immune algorithm[J]. Plos one, 2016, 11(4): e0152600.

[81] Yang C, Ji J, Zhang A. Bacterial biological mechanisms for functional module detection in PPI networks[C]//2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016: 318-323.

[82] Liu J, Ji J, Zhang A, et al. An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data[C]//2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016: 360-367.

[83] 赵学武, 冀俊忠, 梁佩鹏. 面向 fMRI 数据的人脑功能划分[J]. 科学通报, 2016 (18): 2035-2052.

[84] Ji J, Zhang A, Liu C, et al. Survey: Functional module detection from protein-protein interaction networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 26(2): 261-277.

[85] Ji J Z, Jiao L, Yang C C, et al. MAE-FMD: Multi-agent evolutionary method for functional module detection in protein-protein interaction networks[J]. BMC bioinformatics, 2014, 15: 1-26.

[86] 冀俊忠, 刘志军, 刘红欣, 等. 蛋白质相互作用网络功能模块检测的研究综述[J]. 自动化学报, 2014, 40(4): 577-593.

[87] Ji J, Wei H, Liu C. An artificial bee colony algorithm for learning Bayesian networks[J]. Soft Computing, 2013, 17: 983-994.

[88] Ji J, Song X, Liu C, et al. Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks[J]. Physica A: Statistical Mechanics and its Applications, 2013, 392(15): 3260-3272.

[89] Ji J, Liu Z, Zhang A, et al. HAM-FMD: mining functional modules in protein–protein interaction networks using ant colony optimization and multi-agent evolution[J]. Neurocomputing, 2013, 121: 453-469.

[90] 冀俊忠, 魏红凯, 刘椿年, 等. 基于引导素更新和扩散机制的人工蜂群算法[J]. 计算机研究与发展, 2013, 50(9): 2005-2014.

[91] Ji J, Hu R, Zhang H, et al. A hybrid method for learning Bayesian networks based on ant colony optimization[J]. Applied Soft Computing, 2011, 11(4): 3373-3384.

[92] 冀俊忠, 黄振, 刘椿年, 等. 基于多粒度的旅行商问题描述及其蚁群优化算法[J]. 计算机研究与发展, 2010 (3): 434-444.

[93] 冀俊忠, 胡仁兵, 张鸿勋, 等. 一种混合的贝叶斯网结构学习算法[J]. 计算机研究与发展, 2009 (9): 1498-1507.

[94] 冀俊忠, 黄振, 刘椿年. 一种快速求解旅行商问题的蚁群算法[J]. 计算机研究与发展, 2009 (6): 968-978.

[95] 冀俊忠, 黄振, 刘椿年. 基于变异和信息素扩散的多维背包问题的蚁群算法[J]. 计算机研究与发展, 2009 (4): 644-654.

[96] 冀俊忠, 刘椿年, 阎静. 一种快速的贝叶斯网结构学习算法[J]. 计算机研究与发展, 2007, 44(3): 412-419.