Biography

I am currently an associate professor (PhD supervisor) in the College of Computer Science at Beijing University of Technology (BJUT). I obtained my Ph.D. in Computer Science from Beijing University of Technology in June 2020. My research interests are in the general areas of machine learning, data mining and brain science, with a current focus on developing novel techniques to learn the brain effective connectivity networks from neuroimaging data.

Openings: I am looking for motivated students (including PhDs and masters) working in data mining and machine learning. If you are interested in joining my group, please send me your CV.

Publications

“*” Indicates the corresponding author, and “_” indicates a supervised master’s student by me.

2025

• [KDD 2025] Wen Xiong, 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.
• [KBS] Junzhong Ji, Jingdong Fan, Jinduo Liu*. “MsAD-LEC: Estimating Large-scale Brain Effective Connectivity Network based on Multi-subgraph Attention Diffusion”. Knowledge-Based Systems, 309, 112858, 2025.

2024

• [AAAI 2024] Zuozhen Zhang, Junzhong Ji, Jinduo Liu*. “MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity”. The 38th AAAI Conference on Artificial Intelligence (AAAI-24), 38 (9), 10261-10269, 2024.
• [IEEE TMI] 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, 43 (8), 2913-2923, 2024.
• [IJCAI 2024] 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), 3087–3096, 2024.
• [CAAI TRIT] 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, 9(6), 1587–1604, 2024.
• [IEEE TSIPN] 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, 10, 650-664, 2024.
• [IEEE TNSRE] 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, 32, 1132-1141, 2024.
• [IEEE TIM] 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, 73, 1-12, 2024.
• [CIBM] Junzhong Ji, Zuozhen Zhang, Lu Han, Jinduo Liu*. “MetaCAE: Causal Autoencoder with Meta-Knowledge Transfer for Brain Effective Connectivity Estimation”. Computers in Biology and Medicine, 170, 107940, 2024.
• [APIN] Junzhong Ji, Ting Wang, Jinduo Liu*, Muhua Wang, Wei Tang. “River Runoff Causal Discovery with Deep Reinforcement Learning”. Applied Intelligence, 1-19, 2024.
• [IEEE TNNLS] Aixiao Zou, Junzhong Ji, Minglong Lei, Jinduo Liu, Yongduan Song. “Exploring brain effective connectivity networks through spatiotemporal graph convolutional models”. IEEE Transactions on Neural Networks and Learning Systems, 35 (6), 7871-7883, 2024.

2023

• [IEEE MEDAI] Muran Zhu, Xiaotong Huo, Zuozhen Zhang, Jinduo Liu*, Junzhong Ji *. “Arrhythmia Detection from Electrocardiogram Signal Data based on Wavelet Transform and Deep Reinforcement Learning”. IEEE International Conference on Medical Artificial Intelligence, 2023. (Best Student Paper Runner-Up)
• [IEEE TNSRE] 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, 31, 1158-1166, 2023.
• [Bioengineering] Jihao Zhai, Junzhong Ji, Jinduo Liu*. “Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm”. Bioengineering, 10 (8), 909, 2023.
• [Brain Sciences] Zuozhen Zhang, Ziqi Zhang, Junzhong Ji, Jinduo Liu*. “Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data”. Brain sciences, 13 (7), 995, 2023.
• [CAC 2023] Wenxin Zou, Junzhong Ji, Yutong Wang, Jiayi Wang, Yutong Qian, Jinduo Liu*. “Convolutional LSTM with Self-Attention Mechanism for Extreme Weather Prediction”. China Automation Congress (CAC), 6782-6787, 2023.
• [IEEE TKDE] Liuyi Yao, Yaliang Li, Sheng Li, Jinduo Liu, Mengdi Huai, Aidong Zhang and Jing Gao. “Concept-Level Model Interpretation From the Causal Aspect”. IEEE Transactions on Knowledge and Data Engineering, 35(9), 8799-8810, 2023.
• [WWW 2023] Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, and Jing Gao. “Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning”. The Web Conference, Austin, USA, 3680-3688, 2023.
• [IEEE TNNLS] Junzhong Ji, Aixiao Zou, Jinduo Liu, Cuicui Yang, Xiaodan Zhang and Yongduan Song. “A survey on brain effective connectivity network learning”. IEEE Transactions on Neural Networks and Learning Systems, 34(4), 1879-1899, 2023.

2016-2022

• [IEEE TNNLS] Jinduo Liu, Junzhong Ji, Guangxu Xun and Aidong Zhang. “Inferring Effective Connectivity Networks from fMRI Time Series with a Temporal Entropy-score”. IEEE Transactions on Neural Networks and Learning Systems, 33(10), 5993-6006, 2022.
• [IEEE BIBM] Yilin Lu, Jinduo Liu*, Junzhong Ji, Han Lv, Mengdi Huai. “Brain Effective Connectivity Learning with Deep Reinforcement Learning”. 2022 IEEE International Conference on Bioinformatics and Biomedicine, 2022.
• [AAAI 2022] Mengdi Huai, Jinduo Liu, Chenglin Miao, Liuyi Yao and Aidong Zhang. “Towards automating model explanations with certified robustness guarantees”. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6935-6943, 2022.
• [IEEE TMI] Junzhong Ji*, Jinduo Liu*, Lu Han and Feipeng Wan. “Estimating effective connectivity by recurrent generative adversarial networks”. IEEE Transactions on Medical Imaging, 40(12), 3326-3336, 2021.
• [AAAI 2020] 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.
• [IEEE JBHI] 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, 24(7), 2028-2040, 2020.
• [IEEE BIBM] Jinduo Liu, Junzhong Ji, Liuyi Yao and Aidong Zhang. “Estimating brain effective connectivity in fMRI data by non-stationary dynamic Bayesian networks”. 2019 IEEE International Conference on Bioinformatics and Biomedicine, 834-839, 2019.
• [IEEE BIBM] Jinduo Liu, Junzhong Ji, Aidong Zhang and Peipeng Liang. “An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data”. 2016 IEEE International Conference on Bioinformatics and Biomedicine, 360-367, 2016.

Services

Program Committee Member

• The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) Top Reviewers [Top Reviewers]
• The 31th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2025 Research Track August Cycle) EXCELLENT REVIEWER
• The 31th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2025 Research Track February Cycle)
• The Forty-Second International Conference on Machine Learning (ICML 2025)
• The Thirteenth International Conference on Learning Representations (ICLR 2025)
• The 28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025)
• The 39th AAAI Conference on Artificial Intelligence (AAAI 2025)
• 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024)
• The 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2024 Research Track)
• The 2024 ACM International Conference on Multimedia (ACM MM 2024)
• The 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024 Main Track)
• The 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024 Human-Centred AI Track)
• The 2024 Medical Image Computing and Computer-Assisted Intervention (MICCAI 2024)
• SIAM International Conference on Data Mining (SDM 2024)
• The 38th AAAI Conference on Artificial Intelligence (AAAI 2024)
• The 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023 Research Track)
• The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)
• The 2023 Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023)
• The 37th AAAI Conference on Artificial Intelligence (AAAI 2023)
• China Automation Congress (CAC2021;CAC2022;CAC2023)

Journal Reviewer

• IEEE Transactions on Medical Imaging
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Automation Science and Engineering
• IEEE Transactions on Emerging Topics in Computational Intelligence
• SCIENCE CHINA Information Sciences
• IEEE Journal of Biomedical and Health Informatics
• CAAI Transactions on Intelligence Technology
• ACM Transactions on Knowledge Discovery from Data

Contact

Our group is looking for highly motivated PhD and Master students with strong mathematical or programming background [中文招生主页链接].
E-mail: Jinduo AT bjut DOT edu DOT cn