Education

2004.09 - 2007.08: City University of Hong Kong, Ph.D. in Management Sciences

1999.09 - 2002.07: Nankai University, M.S. in Probability Theory and Mathematical Statistics

1995.09 - 1999.06: Nankai University, B.S. in Probability Theory and Mathematical Statistics

 

Work Experience

2016.08 - Present: Professor, School of Statistics, Renmin University of China

2010.08 - 2016.08: Associate Professor, School of Statistics, Renmin University of China

2007.09 - 2010.08: Lecturer, School of Statistics, Renmin University of China

2000.12 - 2004.08: Research Assistant, Department of Management Sciences, City University of Hong Kong

 

Visiting Experience

2012.01 - 2013.01: Visiting Scholar, University of California, Berkeley, USA

 

Research Interests

Machine Learning and Data Science Methods, Graph Neural Networks, Spatiotemporal Trajectory Data Mining, Consumer Behavior Research and Multimodal Review Data Mining, Large Language Model Enhancement

 

Honors and Awards

1. Outstanding Advisor for Undergraduate Thesis, Beijing Education Commission, Sole Recipient, 2023

2. First Prize, Beijing Higher Education Teaching Achievement Award, Beijing People's Government, Sixth Recipient, 2022

3. First Prize, Renmin University of China Teaching Achievement Award, Second Recipient, 2017

4. Advanced Worker, Renmin University of China, 2016

5. Advisor, National College Student Statistical Modeling Competition (Third Prize), China Statistical Education Association, 2015

6. Renmin University of China Teaching Excellence Award, First Recipient, 2014

7. Outstanding Advisor for Bachelor's Degree Thesis in Statistics (National Second Prize, Bachelor's Group), China Statistical Education Association & Xi'an Statistics Research Institute, 2013

8. Renmin University of China Outstanding Undergraduate Thesis Advisor, 2010, 2012

9. Renmin University of China Teaching Achievement Award, Second Prize, Fourth Recipient, 2012

10. Activist in University Trade Union Work, Renmin University of China, 2011

11. Tenth Renmin University of China Outstanding Scientific Research Achievement (Paper Category), First Prize, 2010

12. Top Ten Advisors, Renmin University of China, 2009

13. Renmin University of China Young Faculty Teaching Basic Skills Competition, Excellence Award, 2009

 

Funding

1. Research on Graph Neural Network Models for Social Network Data and its Management Applications, National Natural Science Foundation of China, Grant No.: 72171229, 2022.1-2025.12

2. Research on Varying-Coefficient Multi-Class Models for Real-Time Streaming Data, National Natural Science Foundation of China, Grant No.: 61472475, 2015.1-2018.12

3. Development and Innovation Research on Big Data Phenomena, Theories, and Processing Technologies, Statistical Science Research Institute, National Bureau of Statistics, Project No.: 2013001, 2013-2014

4. Statistical Modeling Research on Consumer Online Shopping Behavior, Renmin University of China Mingde Young Scholar Program, Project No.: 2011030017, 2011.6-2017.6

5. Statistical Modeling Research on Consumer Online Group Buying Behavior, Ministry of Education Humanities and Social Sciences Research Project, Grant No.: 11YJC910004, 2011-2014

 

 

Publications

1. Mengyuan Li, Yuanyuan Zhang, Yaya Zhao, Yalei Du, Xiaoling Lu* (2025), Trajectory representation learning with multilevel attention for driver identification, Expert Systems With Applications, 262, 125580.

2. Jie Song, Xiaoling Lu, Jingya Hong, Feifei Wang* (2025), External information enhancing topic model based on graph neural network, Expert Systems With Applications, 263: 125709.

3. Feifei Wang, Zeyue Zhang, Jie Song, Yixia Yang, Xiaoling Lu*, (2025), Unraveling the anchoring effect of seller’s show on buyer’s show to enhance review helpfulness prediction: A multi-granularity attention network model with multimodal information，Electronic Commerce Research and Applications, 70: 101484.

4. Rui Zhou, Feifei Wang, Chang Liu, Xiaoling Lu* (2025), Sparse dynamic topic model with topic birth and death over time, Knowledge and Information System, online.

5. Feifei Wang, Zimeng Zhao, Ruimin Ye, Xiaoge Gu, Xiaoling Lu* (2025). Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process. Journal of Machine Learning Research. 26: 1-53.

6. Feifei Wang, Xueqiong Yuan, Xiaoling Lu* (2025), Knowledge emerging trend detection using relevance-based dynamic thin topic model, Pattern Analysis and Applications, accepted.

7. Feifei Wang, Zhongtan Lin, Kun Wu, Shuting Han, Libo Sun, Xiaoling Lu* (2024), Multi-order Meta Path Guided Heterogeneous Graph Neural Network for News Recommendation, Systems Engineering — Theory & Practice, 44(5): 1562-1576. (In Chinese)

8. Feifei Wang, Wenjun Liu, Liao Zhu, Xiaoling Lu* (2024), Research on Customer Churn Based on Customer-Service Communication Text Information, Chinese Journal of Management, 21(8): 1199-1207. (In Chinese)

9. Ziliang Li , Yuqian Song, Xiaoling Lu, Miao Liu*(2024)，Twin towers end to end model for aspect-based sentiment analysis，Expert Systems With Applications，249, 123713.

10. Xiaoning Wang, Tao Zhou, Xiaoling Lu*(2024), Multimodal aspect-category sentiment analysis based on multi-level information，IEEE World Congress on Computational Intelligence (IEEE WCCI), 2024

11. Yaya Zhao, Kaiqi Zhao, Zhiqian Chen, Yuanyuan Zhang, Yalei Du, Xiaoling Lu* (2024), A graph-based representation framework for trajectory recovery via spatiotemporal interval-informed Seq2Seq, International Joint Conference on Artificial Intelligence (IJCAI), 2024

12. Hanchao Yan, Xinran Huang, Yiling Ma, Ruizhe Yao, Zhiyu Zhu, Yuanyuan Zhang and Xiaoling Lu* (2023), AttenDenseNet for Sussex-Huawei locomotion-transportation (SHL) recognition challenge, ACM International Conference on Ubiquitous Computing, 2023

13. Yimeng Ren, Xuening Zhu*, Xiaoling Lu and Guyu Hu, (2022), Graphical assistant grouped network autoregression model: a Bayesian nonparametric recourse, Journal of Business & Economic Statistics, 42(1), 49-63.

14. Feifei Wang, Rui Zhou, Yicao Feng, Xiaoling Lu* (2022), Bayesian sparse joint dynamic topic model with flexible lead-lag order, Information Sciences, 616: 392-410.

15. Yandi Zhu, Xiaoling Lu, Jingya Hong, Feifei Wang* (2022), Joint dynamic topic model for recognition of lead-lag relationship in two text corpora, Data Mining and Knowledge Discovery, 36: 2272–2298.

16. Yuxuan Guo, Ming Gao and Xiaoling Lu* (2022), Multivariate change point detection for heterogeneous series, Neurocomputing, (510), 122-134

17. Yuxuan Guo, Feifei Wang, Chen Xing and Xiaoling Lu*, (2022), Mining multi-brand characteristics from online reviews for competitive analysis: A brand joint model using latent Dirichlet allocation, Electronic Commerce Research and Applications，53,1-11

18. Yiwei Fan, Xiaoling Lu, Julong Zhao, Haoda Fu, Yufeng Liu*, (2022) Estimating individualized treatment rules for treatments with hierarchical structure, Electronic Journal of Statistics, 16, 737–784

19. Xiaoling Lu, Yuxuan Guo, Jiayi Chen and Feifei Wang* (2021), Topic change point detection using a mixed Bayesian model, Data Mining and Knowledge Discovery, 36(1): 146-173.

20. Yiwei Fan, Xiaoling Lu, Yufeng Liu, and Julong Zhao* (2020), Angle-based hierarchical classification using exact label embedding. Journal of American Statistical Association, 117(538), 704–717.

21. Yang Yang, Yang Liu, Xiaoling Lu, Jin Xu, Feifei Wang* (2020), A named entity topic model for news popularity prediction, Knowledge-Based Systems (208), 1-12

22. Yiwei Fan and Xiaoling Lu* (2020)，An online Bayesian approach to change-point detection for Categorical data, Knowledge-Based Systems, 196, 1-13

 

Books

1. Lu, X., & Xie, B. (2009). Data Mining Methods and Applications. Renmin University of China Press. (In Chinese)

2. Wu, X., Ma, J., Lu, X., & Yan, J. (2009). Frontier Issues in Data Mining. China Statistics Press. (In Chinese)

3. Mao, S., & Lu, X. (2011). Mathematical Statistics. Renmin University of China Press. (In Chinese)

4. Lu, X. (2015). Statistical Model Analysis and Case Studies of Consumer Behavior. Tsinghua University Press. (In Chinese)

5. Mao, S., & Lu, X. (2016). Mathematical Statistics (2nd Edition). Renmin University of China Press. (In Chinese)

6. Lu, X., & Song, J. (2016). Big Data Mining and Statistical Machine Learning. Renmin University of China Press. (In Chinese)

7. Lu, X., & Song, J. (2019). Big Data Mining and Statistical Machine Learning (2nd Edition). Renmin University of China Press. (In Chinese)

8. Lu, X., & Huang, D. (2021). Statistical Foundations for Data Science. Renmin University of China Press. (In Chinese)

9. Lu, X., & Li, J. (2023). Data Science Practice. Renmin University of China Press. (In Chinese)

10. Lu, X., & Song, J. (2024). Big Data Mining and Statistical Machine Learning (3rd Edition). Renmin University of China Press. (In Chinese)

11. Lu, X., Huang, D., & Li, W. (2024). Statistical Foundations for Data Science (2nd Edition). Renmin University of China Press. (In Chinese)

12. Lu, X. (2024). Text Big Data Analysis Methods and Applications. China Machine Press. (In Chinese)

 

Teaching

Undergraduate Courses: Mathematical Statistics, Data Science Practice, Freshman Seminar, Statistical Computing

Graduate Courses: Applied Statistical Models, Statistical Models and Applications, Big Data Mining and Machine Learning

 

Professional Service

2024.09 - 2028.09: President, Artificial Intelligence Branch, Commerce Statistical Society of China