贾彩燕

基本信息

教育背景

贾彩燕，教授，博士生导师，中国计算机学会《人工智能与模块识别专业委员会》委员，机械工业教育协会人工智能与大数据专委会秘书长，中国人工智能学会《粗糙集与软计算专业委员会》委员，世界中医药学联合会第二界理事会理事，交通数据分析与挖掘北京市重点实验室副主任。2004年于中国科学院计算技术研究所获得计算机软件与理论专业博士学位，师从吴文俊人工智能最高成就奖获得者陆汝钤院士；2004年至2007年在复旦大学上海市智能信息处理实验室做博士后研究；2007年7月就职于北京交通大学计算机与信息技术学院，加入中国计算机学会《人工智能与模块识别专业委员会》主任、计算机学会会士于剑教授研究团队。主要研究方向为：机器学习模型（图神经网络）、虚假信息检测、大模型生成内容检测及可信计算、推荐算法、自动驾驶场景多模态融合目标检测及大模型泛化研究等。目前已在国际国内学术期刊和国际会议上发表论文80余篇。近年来主持国家自然科学基金面上项目两项，国家自然科学基金青年基金面上项目一项，主持国家重点研发计划专项“新一代人工智能”子课题一项。作为团队骨干参加国家自然科学基金重点项目“基于认知的多源学习理论和方法”，参加国家重点研究发计划项目“中医药大数据挖掘研究与创新应用”，参加北京市科委重大项目“图谱化深度学习技术研究及应用验证”等。主持企业合作项目“人工智能在银行支票文字识别中的应用”、“中国民族语文翻译中心蒙古、藏、维吾尔、哈萨克、朝鲜、彝文OCR识别引擎研发”、“卫星遥测异常智能检测系统”、“湖南省水运违法违规智能监测”等多个应用型项目，项目总经费过300万元。获得湖南省科学技术进步二等奖一项，硕士论文获得湖南省百篇优秀硕士论文奖。2011年获北京交通大学“红果园百人计划D类”资助。

工作经历

2016-12 至 今, 北京交通大学, 计算机与信息技术学院, 教授 

2011-11 至 2016-11, 北京交通大学, 计算机与信息技术学院, 副教授

2012-03 至 2013-05, 美国伊利诺伊大学芝加哥分校, 计算机学院，访问学者

2007-07 至 2011-10, 北京交通大学, 计算机与信息技术学院, 讲师

研究方向

• 机器学习与认知计算
• 计算机技术
• 软件工程
• 人工智能
• 大数据技术与工程
• 智能感知与具身智能
• 新一代电子信息技术

招生专业

• 计算机科学与技术硕士
• 计算机技术硕士
• 软件工程硕士
• 人工智能硕士
• 大数据技术与工程硕士
• 计算机科学与技术博士
• 新一代电子信息技术（含量子技术等）硕士
• 软件工程博士
• 新一代电子信息技术（含量子技术等）博士
• 人工智能博士
• 计算机技术博士

科研项目

教学工作

论文/期刊

近5年主要论文成果：

[1] Ziying Song, Lei Yang, Shaoqing Xu, Lin Liu, Dongyang Xu, Caiyan Jia*, Feiyang Jia, Li Wang. GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection. ECCV2024: The 18th European Conference on Computer Vision, Milano, Italy, 2024. (CCF-B，CV三大顶会之一)

[2] Ziying Song, Lin Liu, Feiyang Jia, Yadan Luo, Guoxin Zhang, Lei Yang, Li Wang, Caiyan Jia*. Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook. ArXiv, abs/2401.06542, 2024. (IEEE Transactions on Intelligent Transportation Systems-TITS Minor Revision, SCI, CCF-B)

[3] Ziying Song, Guoxing Zhang, Lin Liu, Lei Yang, Shaoqing Xu, Caiyan Jia*, Feiyang Jia, Li Wang, RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM, International Joint Conference on Artificial Intelligence, Jeju Kearo, Aug3-9, 2024. (CCF-A)

[4] Ziying Song, Caiyan Jia*, Lei Yang, Haiyue Wei, Lin Liu, GraphAlign++: An Accurate Feature Alignment by Graph Matching for Multi-modal 3D Object Detection, IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2023.3306361, 2024. (SCI, CCF-B) 

[5] Chaoqun Cui, Caiyan Jia*, Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection, AAAI’24: The 38th AAAI Conference on Artificial Intelligence, Vancouver, Canada. Feb. 20-27, 2024. (CCF-A)

[6] Yimei Zheng, Caiyan Jia*, ProtoMGAE: Prototype-aware Masked Graph Auto-encoder for Graph Representation Learning, ACM Transactions on Knowledge Discovery in Data, 18(6):1-22, 2024. (SCI, CCF-B)

[7] Yimei Zheng, Caiyan Jia*, Jian Yu, Attributed Graph Clustering Under the Contrastive Mechanism with Cluster-preserving Augmentation, Information Sciences, accept, 2024. (SCI, CCF-B)

[8] Yuhan Qiao, Chaoqun Cui, Yiying Wang, Caiyan Jia*, A Debiased Self-Training Framework with Graph Self-Supervised Pre-training Aided for Semi-Supervised Rumor Detection, NeuroComputing, accepted, 2024. (SCI, CCF-C) 

[9] Ziying Song, Guoxin Zhang, Jun Xie, Lin Liu, Caiyan Jia*, Shaoqing Xu, Zhepeng Wang, VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-modal 3D Object Detection, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3331893, 2024. (SCI, CCF-B)

[10] Ziying Song, Haiyue Wei, Lin Bai, Lei Yang, Caiyan Jia*, GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-modal 3D Obiect Detection, International Conference on Computer Vision, Paris France, Oct. 2-6, 2023. (CCF-A) 

[11] Ziying Song, Haiyue Wei, Caiyan Jia*, Yongchao Xia, Xiaokun Li, Chao Zhang,VP-Net: Voxels as Points for 3-D Object Detection, IEEE Transactions on Geoscience and Remote Sensing, 61: 1-12, 2023. (SCI, CCF-B)

[12] Li Wang, Xinyu Zhang, Ziying Song, Jiangfeng Bi, Guoxin Zhang, Haiyue Wei, Liyao Tang, Lei Yang, Jun Li, Caiyan Jia, and Lijun Zhao. Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy. IEEE Transactions on Intelligent Vehicles, 8, 3781-3798, 2023. (SCI)

[13] Hongyan Ran, Caiyan Jia*, Unsupervised Cross-domain Rumor Detection with Contrastive Learning and Cross-attention, AAAI’23: The 37th AAAI Conference on Artificial Intelligence, Washington, USA. Feb. 7-14, 2023. (CCF-A)

[14] Hongyan Ran, Caiyan Jia*, Jian Yu, A Metric-learning Method for Few-shot Cross-event Rumor Detection, Neurocomputing, 533: 72-85, 2023. (SCI, CCF-C) 

[15] Lin Bai, Xueming Han, Caiyan Jia*, A Rumor Detection Model Incorporating Propagation Path Contextual Semantics and User Information, Neural Processing Letters, 55:9831-9850 , 2023. (SCI, CCF-C)

[16] Yimei Zheng, Caiyan Jia*, Jian Yu, Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks, Pattern Recognition, 139:109469, 2023. (SCI, CCF-B)

[17] Yimei Zheng, Caiyan Jia*, Jian Yu, Contrastive Learning with Cluster-preserving Augmentation for Attributed Graph Clustering, ECML PKDD 2023, LNAI 14169: 644–661, 2023. (CCF-B)

[18] Yongkun Du, Zhineng Chen*, Caiyan Jia, et al, SVTR: Scene Text Recognition with a Single Visual Model, IJCAI 2022, July 23-29, Vienna, Austria, 2022. (CCF-A)

[19] Hongyan Ran, Caiyan Jia*, Pengfei Zhang and Xuanya Li*, MGAT-ESM: Multi-channel Graph Attention Neural Network with Event-sharing Module for Rumor Detection, Information Sciences, 592(11): 402-416, 2022. (SCI, CCF-B)

[20] Pengfei Zhang, Hongyan Ran, Caiyan Jia*, Xuanya Li*, Xueming Han, A Lightweight Propagation Path Aggregating Network with Neural Topic Model for Rumor Detection, Neurocomputing, 458: 468-477, 2021. (SCI, CCF-C)

[21] Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia*, Jian Yu, Traffic Flow Prediction via Spatial Temporal Graph Neural Network, WWW '20: The Web Conference, TaiWan, Apri. 16-20, 2020. (CCF-A，他引489次)

专著/译著

专利

软件著作权

获奖与荣誉

社会兼职