个人简介

  刘金铎，博士，计算机学院，校聘研究员，博士生导师。2020年毕业于北京工业大学获博士学位，2018年和2019年分别在纽约州立大学布法罗分校及弗吉尼亚大学进行博士联合培养，2013年毕业于北京工业大学计算机科学与技术实验班获学士学位。曾获得北京工业大学优秀博士学位论文，北京图象图形学学会优秀博士学位论文提名奖。现任中国人工智能学会因果与不确定性专委会委员，中国图象图形学学会类脑视觉专委会委员。2023年入选北京工业大学高层次人才队伍建设计划（青年优秀人才），2024年入选北京市科技新星。

研究方向

机器学习、数据挖掘、医学图像分析、脑信息学

科研项目

(1) 北京市科技新星，2024.09-2027.09，在研，负责人；

(2) 国家自然科学基金青年项目、北京市教委科技一般项目、中国博士后科学基金面上项目、北京市博士后科研活动经费资助项目、朝阳区博士后科研经费资助项目等，结题，负责人

代表性成果

  近五年，以第一作者/通讯作者发表学术论文20余篇，包括：IEEE TMI、IEEE TNNLS、IEEE TSIPN、IEEE TNSRE、IEEE TIM等期刊，KDD、AAAI、IJCAI等CCF A类会议。授权专利/软著20余项。代表作如下：

(1) Wen Xiong (硕士生), Jinduo Liu*, Junzhong Ji, Fenglong Ma. Brain Effective Connectivity Estimation via Fourier Spatiotemporal Attention. The 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-25), 2025. (CCF A类会议)

(2) Zuozhen Zhang (硕士生), Junzhong Ji, Jinduo Liu*. MetaRLEC: Meta Reinforce- ment Learning for Discovery of Brain Effective Connectivity. The 38th AAAI Conference on Artificial Intelligence (AAAI-24), 38 (9), 10261-10269, 2024. (CCF A类会议)

(3) Jinduo Liu, Feipeng Wang, Junzhong Ji. Concept-Level Causal Explanation Method for Brain Function Network Classification. The 33rd International Joint Conference on Artificial Intelligence (IJCAI-24), 2024. (CCF A类会议)

(4) Jinduo Liu, Junzhong Ji, Guangxu Xun, Liuyi Yao, Mengdi Huai and Aidong Zhang. EC-GAN: inferring brain effective connectivity via generative adversarial networks. The 34th AAAI Conference on Artificial Intelligence (AAAI-20), 34(4), 4852-4859, 2020. (CCF A类会议)

(5) Jinduo Liu, Lu Han, Junzhong Ji. MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning from fMRI and EEG Data. IEEE Transactions on Medical Imaging (TMI), 43 (8), 2913-2923, 2024.

(7) Junzhong Ji, Feipeng Wang, Lu Han, Jinduo Liu*. Causal Learning and Knowledge Fusion Mechanism for Brain Functional Network Classification. IEEE Transactions on Signal and Information Processing over Networks (TSIPN), 2024.

(8) Junzhong Ji, Lu Han, Feipeng Wang, Jinduo Liu*. Dynamic Effective Connectivity Learning based on non-Parametric State Estimation and GAN. IEEE Transactions on Instrumentation and Measurement (TIM), 73, 1-12, 2024.

(9) Han Lv, Jinduo Liu*, Qian Chen, Junzhong Ji, Jihao Zhai, Zuozhen Zhang, Zhaodi Wang, Shusheng Gong, Zhenchang Wang. Brain network evaluation by functional-guided effective connectivity reinforcement learning method indicates therapeutic effect for tinnitus. IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 32, 1132-1141, 2024.

(10) Jinduo Liu, Jihao Zhai, Junzhong Ji. Inferring Causal Protein Signaling Networks from Single-cell Data based on Parallel Discrete Artificial Bee Colony Algorithm. CAAI Transactions on Intelligence Technology (TRIT), 2024.

(11) Jinduo Liu, Junzhong Ji, Xiuqin Jia and Aidong Zhang. Learning brain effective connectivity network structure using ant colony optimization combining with voxel activation information. IEEE journal of biomedical and health informatics (JBHI), 24(7), 2028-2040, 2020.

(12) Junzhong Ji*, Jinduo Liu*, Lu Han and Feipeng Wan. Estimating effective connectivity by recurrent generative adversarial networks. IEEE Transactions on Medical Imaging (TMI), 40(12), 3326-3336, 2021.

(13) Han Lv#, Jinduo Liu#, Qian Chen, Zuozhen Zhang, Zhaodi Wang, Shusheng Gong, Junzhong Ji and Zhenchang Wang. Brain effective connectivity analysis facilitates the treatment outcome expectation of sound therapy in patients with tinnitus. IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 31, 1158-1166, 2023

获奖情况

(1) 指导本科生获得北京市普通高校优秀本科毕业设计（1人）、北京工业大学优秀本科毕业设计（3人）。北京工业大学“立德树人”优秀班主任、科技竞赛优秀指导教师。

(2) 指导硕士研究生获得Best Student Paper Runner-Up (2023 IEEE International Conference on Medical Artificial Intelligence)。指导多名硕士获得研究生国家奖学金。

学术服务

(1) 国际会议程序委员会委员 (PC Member)：NeurIPS (2024-, Top Reviewer), KDD (2023-, Excellent Reviewer), ICML(2025-), ICLR(2025-), AAAI (2023-), IJCAI (2023-), ACM MM (2023-), MICCAI (2023-), SDM (2024-), BIBM (2024-), AISTATS (2025-), ACML (2024-)等

(2) 国际期刊审稿人：IEEE TMI, IEEE TNNLS, IEEE TASE, IEEE TETCI, ACM TKDD等

招生寄语

欢迎来自全国各地喜欢科研的同学报考我们组学术博士、学硕和专硕。亦欢迎对科研创新有兴趣的优秀本科生加入本课题组。让我们一起投身科研，共同进步和发展。