研究方向
1. 大数据分析：因果数据分析及应用
2. 人工智能：① AI因果机器学习及应用（AIGC）② 人机交互技术（智能穿戴技术、VR/AR交互技术）。
重庆大学物联网体感大数据实验室
* 实验室长期招收博士生、硕士生和本科生，有意者请邮件联系。
邮箱：dcsliuli@cqu.edu.cn
学术兼职
1. 人机交互技术应用
【项目1 - Project I】 穿戴式手套及其应用 [video]
【项目2 - Project II】 穿戴设备与VR结合的教育技术应用 [video]
【项目3 - Project III】 AR术中导航系统 [video]
2. AI设计
【项目1 - Project I】 汽车造型智能设计 [link]
【项目2 - Project II】 脸谱艺术智能设计 [link]
3. 数据分析应用
【项目1 - Project I】 发动机生产性能预测 [link]
【项目2 - Project II】 肿瘤风险预测 [link]
* 持续更新中，敬请期待。
近期著作
1. 《因果论：模型、推理和推断(原书第2版)》，朱迪.珀尔[著]，刘礼、李廉、杨矫云、廖军[译]，机械工业出版社，2022.
2. 《因果漫步》，李廉、刘礼、杨矫云、廖军，机械工业出版社，2023.
近期代表性论文
1. 数据分析、因果学习技术及其应用
[2021]*. 刘礼，吴飞，李廉，因果关系学习的思维取向和概念分析，中国大学教学，(10),35-42，2021. [pdf]
[2024]. Zheng, H., Li, Q.S., Chen, S., Liang, Y.X., Liu, L.*, SENCR: A span enhanced two-stage network with counterfactual rethinking for Chinese NER, The 38th AAAI Conference on Artificial Intelligence, AAAI 2024. (CCF A)
[2024]. Li, H.X., Wang, S.Y., Zhang, H.L., Zheng, C.Y., Chen, X., Liu, L., Luo, S.S., Wu, P., Uncovering the limitations of eliminating selection bias for recommendation: missing mechanisms, disentanglement, and identifiability, IEEE International Conference on Data Engineering, ICDE 2024. (CCF A)
[2024]. Li, Z.Y., Huang, S.S., Liu, J.W., Jiang, L.M., Chen, S., Zhang, Y., Liao, J., Wang, S., Liu, L.*, Recognizing Cognitive Load by a Multi-instance Causal Learning Model from Multi-channel Physiological Data, International Conference on Multimedia and Expo, ICME 2024. (CCF B)
[2024]. Jiang, L.M., Liu, J.W., Wang, S., Liao, J., Li, Q.S., Li, Z.Y., Chen, S., Liu, L.*, Multi-channel Spatio-Temporal Causal Representation Model for Cognitive Load Assessment in Physiological Signals, International Conference on Multimedia and Expo, ICME 2024. (CCF B)
[2023]. Huang, S.S., Li, H.X., Li, Q.S., Zheng, C.Y., Liu, L.*, Pareto invariant representation learning for multimedia recommendation, The 31st ACM International Conference on Multimedia, ACM MM, 2023. (CCF A)
[2023]. Wu, J.Y., Hou, L.F., Li, Z.J., Liao, J., Liu, L., Sun, L.Y., Preserving structural consistency in arbitrary artist and artwork style transfer, The 37th AAAI Conference on Artificial Intelligence, AAAI 2023. (CCF A)
[2023]. Huang, S.S., Li, Q.S., Wang, L., Wang, Y.H., Wang, S., Liu, L.*, Score-Based Causal Feature Selection For Cancer Risk Prediction, International Conference on Multimedia and Expo, ICME 2023. (CCF B)
[2022]. Yong, Z.R., Su, G.X., Li, X.H., Sun, L.Y., Li, Z.J., Liu, L.*, Recognizing Cognitive Load by a Hybrid Spatio-Temporal Causal Model from Multivariate Physiological Data, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2022. (CCF B)
[2022]. Deng, Z.Q., Qian, S., Qi, J., Liu, L.*, Xu, B., Recognizing Non-small Cell Lung Cancer Subtypes by a Constraint-Based Causal Network from CT Images, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2022. (CCF B)
[2021]. Luo, H., Liao, J., Yan, X.W., Liu, L.*. Oversampling by a constraint-based causal network in medical imbalanced data classification, International Conference on Multimedia and Expo, ICME 2021. (CCF B) [pdf][slide]
[2020]. Liao, J., Hu, J.F., Liu, L.*, Recognizing Complex Activities by a Temporal Causal Network-Based Model, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020. (CCF B) [pdf]
[2020]. Yan, X.W., Liao, J., Luo, H., Liu, L.*, Predicting cancer risks by a constraint-based causal network, 2020 IEEE International Conference on Multimedia and Expo, ICME 2020. (CCF B) [pdf]
[2019]. He, S., Deng, K., Li, L., Shu, S., Liu, L., Discriminatively Relabel for Partial Multi-label Learning, 2019 IEEE International Conference on Data Mining, ICDM 2019, 280-288, 2019. (CCF B) [pdf]
2. 交互技术及智能识别方法
[2024]. Liao, G.R., Liu, J.W., Liang, Y.X., Wang, S., Liu, L.*, Fall Prediction by a Spatio-Temporal Multi-Channel Causal Model From Wearable Sensors Data, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024. (CCF B)
[2024]. Li, X.H., Liu, J.W., Liao, G.R., Yin, M.R., Wang, S., Su, G.X., Liao, J., Liu, L.*, Predicting Fall Events by a Spatio-Temporal Topological Network With Multiple Wearable Sensors, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024. (CCF B)
[2023]. Hu, J., Liang, Y., Fan, Z., Liu, L.*, Yin, Y., Zimmermann, R., Decoupling long-and short-term patterns in spatiotemporal inference. IEEE Transactions on Neural Networks and Learning Systems, 2023. (CCF B)
[2023]. Liu, J.W., Li, X.H., Liao, G.R., Wang, S., Liu, L.*, MCTN: A Multi-Channel Temporal Network for Wearable Fall Prediction, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2023. (CCF B)
[2023]. Su, G.X., Liu, L.*,Zhang, M.J., Rosenblum, D.S., Quantitative Verification for Monitoring Event-Streaming Systems, IEEE Transactions on Software Engineering, 2022. (CCF A)
[2022]. Wang, S., Wang, A., Ran, M., Liu, L.*, Peng, Y., Liu, M., Alnaim, N., Hand gesture recognition framework using a lie group based spatio-temporal recurrent network with multiple hand-worn motion sensors. Information Sciences, 2022. (CCF B)
[2021]. Li, X., Liao, J., Liu, L.*. Recognizing Skeleton-Based Hand Gestures by a Spatio-Temporal Network, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2021.(CCF B) [pdf][slide]
[2020]. Hu, J.F., Liao, J., Fan, Z.C., Liu, L.*, Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network, The European Conference on Artificial Intelligence, ECAI 2020. (CCF B) [pdf][code]
[2018]. Liu, L., Wang, S., Hu, B., Qiong, Q.Y., Wen, J.H., Rosenblum, D.S., Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition, Pattern Recognition 81:545-561, 2018. (CCF B) [pdf]
[2017]. Liu, L., Wang, S., Su, G.X., Huang, Z.G., Liu, M., Towards complex activity recognition using a Bayesian network-based probabilistic generative framework, Pattern Recognition, 68:295–309, 2017 (CCF B) [pdf]
[2017]. Lu, Y.G., Wei, Y., Liu, L.*, Zhong, J., Sun, L.T., Liu, Y., Towards Unsupervised Physical Activity Recognition Using Smartphone Accelerometers, Multimedia Tools and Applications, 76(8):10701-10719, 2017. (ESI高被引论文) [pdf]
[2017]. Liu, L., Wang, S., Su, G.X., Hu, B., Peng, Y.X., Xiong, Q.Y., Wen, J.H., A framework of mining semantic-based probabilistic event relations for complex activity recognition, Information Sciences, 418-419:13-33, 2017. (CCF B) [pdf]
[2016]. Liu, L., Wang, S., Peng, Y.X., Huang, Z.G., Liu, M., Hu, B., Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty, Patten Recognition, 60:1015-1028, 2016. (CCF B) [pdf]
[2016]. Liu, L., Peng, Y.X., Wang, S., Huang, Z.G., Liu, M., Complex Activity Recognition Using Time Series Pattern Dictionary Learned from Ubiquitous Sensors, Information Sciences, 340–341:41–57, 2016. (CCF B) [pdf]
[2016]. Liu, Y., Nie, L.Q., Liu, L., Rosenblum, D.S., Action to Activity: Sensor-Based Activity Recognition, Neurocomputing, 181:108–115, 2016. (ESI高被引论文)
[2016]. Liu, L., Cheng, L., Liu, Y., Jia, Y.P., Rosenblum, D.S., Recognizing Complex Activities by a Probabilistic Interval-Based Model, The 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 1266- 1272, AAAI press, 2016. (CCF A) [pdf][slide]
[2016]. Jia, Y.P., Song, X.M., Zhou, J.B., Liu, L.*, Nie, L.Q., Rosenblum, D.S., Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction, The 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 165- 171, AAAI press, 2016. (CCF A) [pdf]
3. 智能教育技术应用
[2024]. Liu, M., Zhang, J., Nyagoga L. M., Liu, L., Student-AI question cocreation for enhancing reading comprehension, IEEE Transactions on Learning Technologies, 17, 815-826, 2024.(JCR一区, SSCI)
[2018]. Liu, M., Rus, V., Liu, L., Automatic Chinese multiple choice question generation using mixed similarity strategy, IEEE Transactions on Learning Technologies, 11(2):193-202, 2018. (JCR一区, SSCI) [pdf]
[2018]. Liu, M., Liu, L.P., Rus, V., Liu, L., Group awareness increases student engagement in online collaborative writing, The Internet and Higher Education, 38:1-8, 2018. (JCR一区, SSCI) [pdf]
[2017]. Liu, M., Rus, V., Liu, L., Automatic Chinese Factual Question Generation, IEEE Transactions on Learning Technologies, 10(2):194-204, 2017. (JCR一区, SSCI) [pdf]
[2017]. Liu, M., Li, Y., Xu, W.W., Liu, L., Automated Essay Feedback Generation and Its Impact in the Revision, IEEE Transactions on Learning Technologies, 10(4): 502-513, 2017. (JCR一区, SSCI) [pdf]
近期主要项目
1. 国家重点研发计划（课题）“设计信息统一表达与创新方法融合集成研究” 2022.11-2025.10 主持
2. 国家自然科学基金委面上项目 “基于生理特征感知的具身学习认知负荷智能测评研究” 2020.01-2023.12 主持
3. 国家“新一代人工智能”重大科技专项 “基于跨媒体知识图谱的因果计算 （课题：基于因果图谱的跨媒体创意设计）” 2020.01-2022.12 子课题主持
4. 国家自然科学基金委青年项目 “普适环境下支持生物电信号的多模态情感特征建模和推理研究” 2011.01-2013.12 主持
5. 国家重点研发计划 “综合科技服务资源集成应用关键技术研究” 2018.05-2020.11 主研
6. 工信部科技计划项目 “长安汽车智能柔性高速冲压新模式应用” 2017.06-2018.06 主研
成果获奖
1. “大数据驱动的智慧建筑工地信息管理平台关键技术与综合应用” 华夏建设科学技术奖（省部级）二等奖 2019
2. AI2000人工智能全球最具影响力学者（全球排名57名）, AI Times, AMiner评选，2022
授权发明专利
1. “一种大数据分析发动机冷试检测数据与工位相关性的方法” ZL201811474787.X
2. “实时手势识别方法及系统” ZL201910414488.5
3. “一种实时动态追踪的游戏手套” ZL201910822750.X
4. “一种大数据分析模型预测发动机性能的方法” ZL201811476008.X
5. “一种基于李群和长短时记忆网络的手势识别方法” ZL202010471688.7
6. “一种基于深度学习的手势跟踪与识别方法” ZL202010452860.4
7. “结合数据手套和VR技术的手功能康复系统” ZL202110724588.5
8. “一种实时追踪人体下肢运动的穿戴设备” ZL202110971693.9
9. “基于与模型无关局部解释的在线购物代表性样本选择系统” ZL202010453195.0
10. “手势数据采集手套及基于手势数据采集手套的手语手势识别方法” ZL202010439044.X
11. “基于因果流模型的可控汽车图像合成方法” ZL202010942153.3
12. “基于生成对抗网络GAN自动生成水墨画的方法” ZL202010532759.X
13. “一种基于大数据分析技术的胃癌疾病风险检测装置” ZL201910828098.2
14. “一种患肝癌风险等级快速预测装置” ZL201910828869.8
专业任职经历
2010/05 至今，中国计算机学会协同专业委员会，委员
2016/11 至 2021/10，中国计算机学会生物信息学专业组，委员
2018/09 至今，中国计算机学会普适计算专业委员会，委员
2018/05 至今，重庆大学物联网体感大数据实验室，主任
2019/04 至今，中国生物医学工程学会中医药工程分会，委员
2021/06 至今，重庆市数字影视艺术理论与技术重点实验室，副主任
2022/04 至今，中国高等教育学会学习科学研究分会，理事
2023/05 至今，中国人工智能学会智能创意与数字艺术专委会，副主任
