研究方向
机器学习、计算智能、生物信息挖掘、脑影像智能分析
个人简介
男，博士，教授，博士生导师
教育简历
2004年在北京工业大学获得计算机应用技术专业博士学位，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，2012年被评聘为博士研究生导师。
学术兼职
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。
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）
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。
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9月，获得 "2018年北京工业大学优秀教师标兵" 称号；
2018年11月, 获得"北京工业大学研究生教育突出贡献奖"；
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。
代表性研究成果
已在TKDE、TIP、TNNLS、TMI、TITS、AAAI、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170多篇。
主要论文论著
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] Ji J, Weng Y, Yang C. Multiobjective bacterial foraging optimization with an adaptive dual grid strategy[J]. Swarm and Evolutionary Computation，2022,72: 101098.
[10] 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.
[11] Ji J, Wang M, Zhang X, et al. Relation constraint self-attention for image captioning[J]. Neurocomputing, 2022, 501: 778-789.
[12] Ji J, Ren Y, Lei M. FC–HAT: Hypergraph attention network for functional brain network classification[J]. Information Sciences, 2022, 608: 1301-1316.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] Ji J, Du Z, Zhang X. Divergent-convergent attention for image captioning[J]. Pattern Recognition, 2021, 115: 107928.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] 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.
[32] 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.
[33] 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.
[34] 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.
[35] 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.
[36] 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.
[37] 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.
[38] 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.
[39] 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.
[40] 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.
[41] 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.
[42] 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.
[43] 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.
[44] 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.
[45] Yang C, Ji J, Liu J, et al. Bacterial foraging optimization using novel chemotaxis and conjugation strategies[J]. Information Sciences, 2016, 363: 72-95.
[46] 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.
[47] 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.
[48] 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.
[49] 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.
[50] 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.
[51] 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.
[52] 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.
[53] 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.
[54] Ji J, Wei H, Liu C. An artificial bee colony algorithm for learning Bayesian networks[J]. Soft Computing, 2013, 17: 983-994.
[55] 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.
[56] 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.
[57] 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.
[58] 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.
[59] 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.
[60] 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.
[61] 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.
[62] 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.
[63] 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.
